DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is responsive to the Amendment filed on 7/1/2026. Claims 1, 4, 7-8, 13-21, 23, 25-27, and 29 are pending in the case. Claims 2 and 10 have been cancelled. Claims 1, 14, and 25 are independent claims.
Claim Interpretation
Each independent claim recites a Markush group of four alternatives “at least one of identity information, data metadata information, model parameter information and model metadata information” followed by language further limiting each of the alternatives. Only one (selected limitation) of the alternatives must to be taught by the prior art to properly reject the claims. Subsequent limitations further limiting the non-selected limitations are not required to be taught by the prior art.
Response to Arguments
Applicant’s amendments regarding the drawing objections are persuasive. These objections are respectfully withdrawn.
Applicant’s prior art arguments have been fully considered but they are not persuasive.
Applicant argues that “In Zhang, the client machine 204 itself judges whether it is eligible to join federated learning and transmits a participation request to the server only if self-verification passes. In contrast, in embodiments of the present invention as claimed, e.g., in claim 1, eligibility verification for participation is performed via a blockchain. The verification is conducted on the blockchain in a more secure and impartial manner rather than by the participant node itself, which guarantees accuracy in identifying federated learning participants” (page 17). Examiner respectfully disagrees. As cited in the previous Office action, the server may conduct its own verification on blockchain (“the requests to participate in the training of the global machine-learning model may be published to the blockchain such that a central server and/or other clients may review the requests and identify which clients may participate in the training of the global machine-learning model,” paragraph 0059 lines 8-13).
Applicant argues that “the self-conducted verification performed by the client in Zhang differs from the participation eligibility verification based on multiple types of information. Specifically, embodiments of the invention as claimed, e.g., in claim 1,perform [sic] verification based on combination of identity information, and at least one of data metadata information, model parameter information and model metadata information of federated learning applicants, and the data metadata information particularly excludes original data for federated learning” (page 17). Examiner respectfully disagrees:
Each independent claim recites a Markush group of four alternatives: “at least one of identity information, data metadata information, model parameter information and model metadata information.” The claims do not require both the identity information alternative and one of the other three alternatives. The non-selected alternatives and subsequent limitations further limiting the non-selected alternatives are not required to be taught by the prior art.
As cited in the previous Office action, the eligibility verification may be based on multiple types of information (“A client-selection policy 122 that includes one or more criteria for determining the suitability of the clients 110 for training the global machine-learning model may be stored on the central server 120. The criteria for determining the suitability of a given client 110 for training the global machine-learning model may relate to one or more measurements of the relevance of the local dataset of the given client 110 to the training tasks associated with the global machine-learning model and/or one or more measurements of the capability of the given client 110 to perform the training tasks associated with the global machine-learning model,” paragraph 0029 lines 1-12).
As newly cited below, the eligibility verification may be based on all four alternatives (“The blockchain 130 may include metadata that describes the global machine-learning model of the central server 120, the clients 110, and/or the local datasets of the clients 110. For example, the metadata published to the blockchain 130 may include one or more metadata fields, such as a training task identifier, a training round identifier, a client identifier, a number of training samples (e.g., data entries) included in a given local dataset, a training accuracy of a locally trained machine-learning model and/or a global machine-learning model, a testing accuracy of a locally trained machine-learning model and/or a global machine-learning model, a file name of the global machine-learning model, a threshold number of clients required for a given training round, a threshold training accuracy required for a given training round, etc.,” paragraph 0033 lines 1-15).
Applicant argues that “Zhang neither considers nor teaches verifying node eligibility to participate in federated learning by leveraging model parameter information or model metadata information” (page 18). Examiner respectfully disagrees. The eligibility verification may leverage model parameter information and model metadata information (“A client-selection policy 122 that includes one or more criteria for determining the suitability of the clients 110 for training the global machine-learning model may be stored on the central server 120. The criteria for determining the suitability of a given client 110 for training the global machine-learning model may relate to one or more measurements of the relevance of the local dataset of the given client 110 to the training tasks associated with the global machine-learning model and/or one or more measurements of the capability of the given client 110 to perform the training tasks associated with the global machine-learning model,” paragraph 0029 lines 1-12).
Applicant argues that “the technical problem addressed by the present application is the lack of mutual trust among federated learning participants: the participants in federated learning come from different organizations or institutions and lack trust with each other, and thus it is difficult to establish a safe and reliable cooperation mechanism in the absence of mutual trust. The technical problem to be solved and inventive concept of Zhang differ markedly from those of the present application” (page 18). Examiner respectfully disagrees. Zhang allows both the server and other nodes to admit nodes through blockchain (“the requests to participate in the training of the global machine-learning model may be published to the blockchain such that a central server and/or other clients may review the requests and identify which clients may participate in the training of the global machine-learning model,” paragraph 0059 lines 8-13).
Therefore, Examiner respectfully asserts that the cited art sufficiently teaches the limitations recited in the amended claims.
Claim Rejections - 35 U.S.C. § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. §§ 102 and 103 (or as subject to pre-AIA 35 U.S.C. §§ 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. § 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 4, 7-8, 13-21, 23, 25-27, and 29 are rejected under 35 U.S.C. § 102(a)(1) as being anticipated by Zhang et al. (US 2022/0044162 A1, hereinafter Zhang).
As to independent claim 1, Zhang discloses an electronic device for blockchain-based federated learning, comprising a processing circuit configured to:
acquire first federated learning related information from a federated learning node (“At block 420, requests to participate in training the global machine-learning model may be received from one or more clients,” paragraph 0059 lines 1-3);
cause verifying whether at least one piece of information contained in the first federated learning related information meets a corresponding federated learning requirement (“A client-selection policy 122 that includes one or more criteria for determining the suitability of the clients 110 for training the global machine-learning model may be stored on the central server 120. The criteria for determining the suitability of a given client 110 for training the global machine-learning model may relate to one or more measurements of the relevance of the local dataset of the given client 110 to the training tasks associated with the global machine-learning model and/or one or more measurements of the capability of the given client 110 to perform the training tasks associated with the global machine-learning model,” paragraph 0029 lines 1-12) through blockchain (“the requests to participate in the training of the global machine-learning model may be published to the blockchain such that a central server and/or other clients may review the requests and identify which clients may participate in the training of the global machine-learning model,” paragraph 0059 lines 8-13), and confirm that the federated learning node is able to participate in federated learning (“A group of clients may be selected as qualified clients to participate in the training of the global machine-learning model based on the requests to participate,” paragraph 0059 lines 13-16), in response to the at least one piece of information contained in the first federated learning related information meeting the corresponding federated learning requirement (“A client-selection policy 122 that includes one or more criteria for determining the suitability of the clients 110 for training the global machine-learning model may be stored on the central server 120. The criteria for determining the suitability of a given client 110 for training the global machine-learning model may relate to one or more measurements of the relevance of the local dataset of the given client 110 to the training tasks associated with the global machine-learning model and/or one or more measurements of the capability of the given client 110 to perform the training tasks associated with the global machine-learning model,” paragraph 0029 lines 1-12); and
notify the federated learning side of indication information indicating federated learning nodes that are able to participate in federated learning, wherein the indicated federated learning nodes that are able to participate in federated learning are able to perform data processing based on federated learning (“A group of clients may be selected as qualified clients to participate in the training of the global machine-learning model based on the requests to participate,” paragraph 0059 lines 13-16);
wherein the at least one information comprises at least one of identity information, data metadata information, model parameter information and model metadata information of the federated learning node (“The blockchain 130 may include metadata that describes the global machine-learning model of the central server 120, the clients 110, and/or the local datasets of the clients 110. For example, the metadata published to the blockchain 130 may include one or more metadata fields, such as a training task identifier, a training round identifier, a client identifier, a number of training samples (e.g., data entries) included in a given local dataset, a training accuracy of a locally trained machine-learning model and/or a global machine-learning model, a testing accuracy of a locally trained machine-learning model and/or a global machine-learning model, a file name of the global machine-learning model, a threshold number of clients required for a given training round, a threshold training accuracy required for a given training round, etc.,” paragraph 0033 lines 1-15), wherein in response to identity verification of the federated learning node passing, and at least one of data metadata information, model parameter information and model metadata information of the federated learning node meeting the corresponding federated learning requirement, the federated learning node is able to participate in federated learning (“A client-selection policy 122 that includes one or more criteria for determining the suitability of the clients 110 for training the global machine-learning model may be stored on the central server 120. The criteria for determining the suitability of a given client 110 for training the global machine-learning model may relate to one or more measurements of the relevance of the local dataset of the given client 110 to the training tasks associated with the global machine-learning model and/or one or more measurements of the capability of the given client 110 to perform the training tasks associated with the global machine-learning model,” paragraph 0029 lines 1-12);
wherein, the data metadata information includes at least one of data attribute information, data structure information and data distribution information (“A client-selection policy 122 that includes one or more criteria for determining the suitability of the clients 110 for training the global machine-learning model may be stored on the central server 120. The criteria for determining the suitability of a given client 110 for training the global machine-learning model may relate to one or more measurements of the relevance of the local dataset of the given client 110 to the training tasks associated with the global machine-learning model and/or one or more measurements of the capability of the given client 110 to perform the training tasks associated with the global machine-learning model,” paragraph 0029 lines 1-12);
the model parameter information includes at least one of model type, model weight and model gradient (“the clients 110 may publish metadata to the blockchain 130 in which the published metadata describes the local datasets of the clients 110 and/or the clients 110 themselves,” paragraph 0020 lines 3-6); and
wherein, the model metadata information includes at least one of an identifier of a federated learning participant node, a model type, the amount of local training samples, local model training accuracy, and the federated learning participation status information (“A client-selection policy 122 that includes one or more criteria for determining the suitability of the clients 110 for training the global machine-learning model may be stored on the central server 120. The criteria for determining the suitability of a given client 110 for training the global machine-learning model may relate to one or more measurements of the relevance of the local dataset of the given client 110 to the training tasks associated with the global machine-learning model and/or one or more measurements of the capability of the given client 110 to perform the training tasks associated with the global machine-learning model,” paragraph 0029 lines 1-12).
As to dependent claim 4, Zhang further discloses a device wherein the processing circuit is further configured to:
acquire model parameters from federated learning participant nodes on the federated learning side (“At block 430, one or more local model updates may be obtained from the clients participating in the training of the global machine-learning model,” paragraph 0060 lines 1-3);
cause generation of model blocks based on the model parameters from the federated learning participant nodes (“At block 440, the local model updates obtained by the central server may be aggregated as an aggregated local model update as described in relation to FIG. 2. At block 450, the global machine-learning model may be updated based on the aggregated local model update as described in relation to FIG. 2,” paragraph 0061 lines 1-3) through blockchain (“At operations 316, metadata of the masked local model updates may be published to the blockchain 306,” paragraph 0053 lines 1-2); and
notify the generation of model blocks to the federated learning side (“At operations 208, metadata associated with the global machine-learning model of the central server 202 may be published to the blockchain 206,” paragraph 0039 lines 1-3),
wherein the model blocks comprise one of sub model blocks and a global model block generated based on the sub model blocks (“At block 440, the local model updates obtained by the central server may be aggregated as an aggregated local model update as described in relation to FIG. 2. At block 450, the global machine-learning model may be updated based on the aggregated local model update as described in relation to FIG. 2,” paragraph 0061 lines 1-3); and
wherein the federated learning participant nodes optimize model parameters based on the model blocks (“At block 440, the local model updates obtained by the central server may be aggregated as an aggregated local model update as described in relation to FIG. 2. At block 450, the global machine-learning model may be updated based on the aggregated local model update as described in relation to FIG. 2,” paragraph 0061 lines 1-3).
As to dependent claim 7, Zhang further discloses a device wherein the processing circuit is further configured to:
generate sub-model blocks based on model parameters from federated learning participant nodes (“At block 440, the local model updates obtained by the central server may be aggregated as an aggregated local model update as described in relation to FIG. 2. At block 450, the global machine-learning model may be updated based on the aggregated local model update as described in relation to FIG. 2,” paragraph 0061 lines 1-3) through blockchain (“At operations 316, metadata of the masked local model updates may be published to the blockchain 306,” paragraph 0053 lines 1-2), and
cause aggregation of the generated sub-model blocks through a blockchain technique to generate a global model block (“At block 440, the local model updates obtained by the central server may be aggregated as an aggregated local model update as described in relation to FIG. 2. At block 450, the global machine-learning model may be updated based on the aggregated local model update as described in relation to FIG. 2,” paragraph 0061 lines 1-3).
As to dependent claim 8, Zhang further discloses a device wherein the processing circuit is further configured to:
acquire third (“one or more training rounds of the global machine-learning model,” paragraph 0014 lines 8-9) federated learning related information from the federated learning nodes (“At block 420, requests to participate in training the global machine-learning model may be received from one or more clients,” paragraph 0059 lines 1-3),
cause verifying whether to initiate execution of federated learning through blockchain based on the third federated learning related information (“A client-selection policy 122 that includes one or more criteria for determining the suitability of the clients 110 for training the global machine-learning model may be stored on the central server 120. The criteria for determining the suitability of a given client 110 for training the global machine-learning model may relate to one or more measurements of the relevance of the local dataset of the given client 110 to the training tasks associated with the global machine-learning model and/or one or more measurements of the capability of the given client 110 to perform the training tasks associated with the global machine-learning model,” paragraph 0029 lines 1-12) through blockchain (“the requests to participate in the training of the global machine-learning model may be published to the blockchain such that a central server and/or other clients may review the requests and identify which clients may participate in the training of the global machine-learning model,” paragraph 0059 lines 8-13), and
transmit at least one part of the third federated learning related information to the federated learning side (“A group of clients may be selected as qualified clients to participate in the training of the global machine-learning model based on the requests to participate,” paragraph 0059 lines 13-16), in a case that it is verified that federated learning through blockchain (“the requests to participate in the training of the global machine-learning model may be published to the blockchain such that a central server and/or other clients may review the requests and identify which clients may participate in the training of the global machine-learning model,” paragraph 0059 lines 8-13) is able to be initiated (“A client-selection policy 122 that includes one or more criteria for determining the suitability of the clients 110 for training the global machine-learning model may be stored on the central server 120. The criteria for determining the suitability of a given client 110 for training the global machine-learning model may relate to one or more measurements of the relevance of the local dataset of the given client 110 to the training tasks associated with the global machine-learning model and/or one or more measurements of the capability of the given client 110 to perform the training tasks associated with the global machine-learning model,” paragraph 0029 lines 1-12), wherein the third federated learning related information is at least partially the same type as information contained in the first federated learning related information (“one or more training rounds of the global machine-learning model,” paragraph 0014 lines 8-9).
As to dependent claim 13, Zhang further discloses a device wherein at least one of the federated learning nodes belongs to a node in the blockchain side (“the requests to participate in the training of the global machine-learning model may be published to the blockchain such that a central server and/or other clients may review the requests and identify which clients may participate in the training of the global machine-learning model,” paragraph 0059 lines 8-13).
As to independent claim 14, Zhang discloses an electronic device for blockchain-based federated learning, comprising a processing circuit configured to:
transmit first federated learning related information (“At block 420, requests to participate in training the global machine-learning model may be received from one or more clients,” paragraph 0059 lines 1-3) to a blockchain side (“the requests to participate in the training of the global machine-learning model may be published to the blockchain such that a central server and/or other clients may review the requests and identify which clients may participate in the training of the global machine-learning model,” paragraph 0059 lines 8-13);
acquire indication information from the blockchain side indicating whether a federated learning node associated with the electronic device is able to participate in federated learning, wherein the indication information is generated by verifying the first federated learning related information (“A client-selection policy 122 that includes one or more criteria for determining the suitability of the clients 110 for training the global machine-learning model may be stored on the central server 120. The criteria for determining the suitability of a given client 110 for training the global machine-learning model may relate to one or more measurements of the relevance of the local dataset of the given client 110 to the training tasks associated with the global machine-learning model and/or one or more measurements of the capability of the given client 110 to perform the training tasks associated with the global machine-learning model,” paragraph 0029 lines 1-12) through blockchain (“the requests to participate in the training of the global machine-learning model may be published to the blockchain such that a central server and/or other clients may review the requests and identify which clients may participate in the training of the global machine-learning model,” paragraph 0059 lines 8-13); and
under the condition that it is determined that the federated learning node associated with the electronic device is able to participate in federated learning based on the indication information (“A group of clients may be selected as qualified clients to participate in the training of the global machine-learning model based on the requests to participate,” paragraph 0059 lines 13-16), cause data processing by federated learning nodes on the federated learning side that are able to participate in the federated learning in combination based on federated learning (“At block 430, one or more local model updates may be obtained from the clients participating in the training of the global machine-learning model,” paragraph 0060 lines 1-3);
wherein verifying the first federated learning related information through blockchain, comprises:
verifying whether at least one piece of information contained in the first federated learning related information meets the corresponding federated learning requirement (“A client-selection policy 122 that includes one or more criteria for determining the suitability of the clients 110 for training the global machine-learning model may be stored on the central server 120. The criteria for determining the suitability of a given client 110 for training the global machine-learning model may relate to one or more measurements of the relevance of the local dataset of the given client 110 to the training tasks associated with the global machine-learning model and/or one or more measurements of the capability of the given client 110 to perform the training tasks associated with the global machine-learning model,” paragraph 0029 lines 1-12) through blockchain (“the requests to participate in the training of the global machine-learning model may be published to the blockchain such that a central server and/or other clients may review the requests and identify which clients may participate in the training of the global machine-learning model,” paragraph 0059 lines 8-13),
wherein the at least one information comprises at least one of identity information, data metadata information, model parameter information and model metadata information of the federated learning node (“The blockchain 130 may include metadata that describes the global machine-learning model of the central server 120, the clients 110, and/or the local datasets of the clients 110. For example, the metadata published to the blockchain 130 may include one or more metadata fields, such as a training task identifier, a training round identifier, a client identifier, a number of training samples (e.g., data entries) included in a given local dataset, a training accuracy of a locally trained machine-learning model and/or a global machine-learning model, a testing accuracy of a locally trained machine-learning model and/or a global machine-learning model, a file name of the global machine-learning model, a threshold number of clients required for a given training round, a threshold training accuracy required for a given training round, etc.,” paragraph 0033 lines 1-15), wherein in response to identity verification of the federated learning node passing, and at least one of data metadata information, model parameter information and model metadata information of the federated learning node meeting the corresponding federated learning requirement, the federated learning node is able to participate in federated learning (“A client-selection policy 122 that includes one or more criteria for determining the suitability of the clients 110 for training the global machine-learning model may be stored on the central server 120. The criteria for determining the suitability of a given client 110 for training the global machine-learning model may relate to one or more measurements of the relevance of the local dataset of the given client 110 to the training tasks associated with the global machine-learning model and/or one or more measurements of the capability of the given client 110 to perform the training tasks associated with the global machine-learning model,” paragraph 0029 lines 1-12);
wherein, the data metadata information includes at least one of data attribute information, data structure information and data distribution information (“A client-selection policy 122 that includes one or more criteria for determining the suitability of the clients 110 for training the global machine-learning model may be stored on the central server 120. The criteria for determining the suitability of a given client 110 for training the global machine-learning model may relate to one or more measurements of the relevance of the local dataset of the given client 110 to the training tasks associated with the global machine-learning model and/or one or more measurements of the capability of the given client 110 to perform the training tasks associated with the global machine-learning model,” paragraph 0029 lines 1-12);
the model parameter information includes at least one of model type, model weight and model gradient (“the clients 110 may publish metadata to the blockchain 130 in which the published metadata describes the local datasets of the clients 110 and/or the clients 110 themselves,” paragraph 0020 lines 3-6); and
wherein, the model metadata information includes at least one of an identifier of a federated learning participant node, a model type, the amount of local training samples, local model training accuracy, and the federated learning participation status information (“A client-selection policy 122 that includes one or more criteria for determining the suitability of the clients 110 for training the global machine-learning model may be stored on the central server 120. The criteria for determining the suitability of a given client 110 for training the global machine-learning model may relate to one or more measurements of the relevance of the local dataset of the given client 110 to the training tasks associated with the global machine-learning model and/or one or more measurements of the capability of the given client 110 to perform the training tasks associated with the global machine-learning model,” paragraph 0029 lines 1-12).
As to dependent claim 15, Zhang further discloses a device wherein the processing circuit is further configured to:
acquire global model parameters, which are generated based on model parameters of federated learning nodes on the federated learning side that are able to participate in federated learning (“At block 440, the local model updates obtained by the central server may be aggregated as an aggregated local model update as described in relation to FIG. 2. At block 450, the global machine-learning model may be updated based on the aggregated local model update as described in relation to FIG. 2,” paragraph 0061 lines 1-3); and
perform model optimization based on the global model parameters (“At block 440, the local model updates obtained by the central server may be aggregated as an aggregated local model update as described in relation to FIG. 2. At block 450, the global machine-learning model may be updated based on the aggregated local model update as described in relation to FIG. 2,” paragraph 0061 lines 1-3).
As to dependent claim 16, Zhang further discloses a device wherein the processing circuit is further configured to:
acquire model parameters of federated learning nodes on the federated learning side that are able to participate in federated learning (“At block 440, the local model updates obtained by the central server may be aggregated as an aggregated local model update as described in relation to FIG. 2. At block 450, the global machine-learning model may be updated based on the aggregated local model update as described in relation to FIG. 2,” paragraph 0061 lines 1-3),
generate global model parameters by aggregating the model parameters of federated learning nodes (“At block 440, the local model updates obtained by the central server may be aggregated as an aggregated local model update as described in relation to FIG. 2. At block 450, the global machine-learning model may be updated based on the aggregated local model update as described in relation to FIG. 2,” paragraph 0061 lines 1-3), and
transmit the global model parameters to each federated learning node (“At operations 208, metadata associated with the global machine-learning model of the central server 202 may be published to the blockchain 206,” paragraph 0039 lines 1-3).
As to dependent claim 17, Zhang further discloses a device wherein the processing circuit is further configured to:
transmit model parameters acquired by the federated learning node through local model training (“At operations 316, metadata of the masked local model updates may be published to the blockchain 306,” paragraph 0053 lines 1-2), and
acquire a global model block, which is generated based on model parameters of federated learning nodes on the federated learning side that are able to participate in the federated learning through blockchain (“At operations 208, metadata associated with the global machine-learning model of the central server 202 may be published to the blockchain 206,” paragraph 0039 lines 1-3); and
perform local model optimization based on the global model block (“The central server 202 may send data associated with the global machine-learning model to the clients 204 capable of performing the training tasks at operations 216 such that the respective local dataset corresponding to each of the clients 204 may be used to train the global machine-learning model at operations 214,” paragraph 0042 lines 5-11).
As to dependent claim 18, Zhang further discloses a device wherein the processing circuit is further configured to:
acquire sub-model blocks generated based on model parameters of federated learning nodes (“At block 440, the local model updates obtained by the central server may be aggregated as an aggregated local model update as described in relation to FIG. 2. At block 450, the global machine-learning model may be updated based on the aggregated local model update as described in relation to FIG. 2,” paragraph 0061 lines 1-3) through blockchain (“At operations 316, metadata of the masked local model updates may be published to the blockchain 306,” paragraph 0053 lines 1-2); and
aggregate the acquired sub-model blocks into a global model block (“At block 440, the local model updates obtained by the central server may be aggregated as an aggregated local model update as described in relation to FIG. 2. At block 450, the global machine-learning model may be updated based on the aggregated local model update as described in relation to FIG. 2,” paragraph 0061 lines 1-3).
As to dependent claim 19, Zhang further discloses a device wherein the processing circuit is further configured to: distribute the global model block to other federated learning nodes that are able to participate in federated learning (“The central server 202 may send data associated with the global machine-learning model to the clients 204 capable of performing the training tasks at operations 216 such that the respective local dataset corresponding to each of the clients 204 may be used to train the global machine-learning model at operations 214,” paragraph 0042 lines 5-11).
As to dependent claim 20, Zhang further discloses a device wherein the processing circuit is further configured to:
acquire second (“one or more training rounds of the global machine-learning model,” paragraph 0014 lines 8-9) federated learning related information (“In response to one or more of the clients 204 determining that its respective local dataset is relevant to performing the training tasks, the clients 204 including relevant local datasets may request to participate in training the global-machine learning model at operations 214,” paragraph 0042 lines 1-5),
verify whether local federated learning related information matches the acquired second federated learning related information (“In response to one or more of the clients 204 determining that its respective local dataset is relevant to performing the training tasks, the clients 204 including relevant local datasets may request to participate in training the global-machine learning model at operations 214,” paragraph 0042 lines 1-5); and
in the case of matching, determine that the federated learning node associated with the electronic device intends to participate in federated learning (“In response to one or more of the clients 204 determining that its respective local dataset is relevant to performing the training tasks, the clients 204 including relevant local datasets may request to participate in training the global-machine learning model at operations 214,” paragraph 0042 lines 1-5).
As to dependent claim 21, Zhang further discloses a device wherein the processing circuit is further configured to:
transmit a federated learning initiation request to the blockchain side, wherein the federated learning initiation request includes third federated learning related information (“In response to one or more of the clients 204 determining that its respective local dataset is relevant to performing the training tasks, the clients 204 including relevant local datasets may request to participate in training the global-machine learning model at operations 214,” paragraph 0042 lines 1-5); and
acquire information indicating whether the blockchain-based federated learning is allowed to be initiated, wherein the information is generated by verifying the third federated learning related information through blockchain (“the requests to participate in the training of the global machine-learning model may be published to the blockchain such that a central server and/or other clients may review the requests and identify which clients may participate in the training of the global machine-learning model,” paragraph 0059 lines 8-13).
As to dependent claim 23, Zhang further discloses a device wherein the processing circuit is configured to:
verify whether model parameters meet a specific convergence condition (“determining the updated global machine-learning model may indicate an end of a given training round,” paragraph 0046 lines 7-9), and
perform the blockchain-based federated learning iteratively, if the model parameters cannot meet the specific convergence condition (“the central server 202 may include a threshold number of training rounds (e.g., a minimum number of training rounds), and the operations 200 may be repeated until the threshold number of training rounds is satisfied,” paragraph 0046 lines 16-20).
As to independent claim 25, Zhang discloses a method of blockchain-based federated learning, which is executed in a blockchain-based federated learning system including a federated learning side and a blockchain side, the method comprises:
transmitting first federated learning related information associated with federated learning nodes from the federated learning side (“At block 420, requests to participate in training the global machine-learning model may be received from one or more clients,” paragraph 0059 lines 1-3) to the blockchain side (“the requests to participate in the training of the global machine-learning model may be published to the blockchain such that a central server and/or other clients may review the requests and identify which clients may participate in the training of the global machine-learning model,” paragraph 0059 lines 8-13);
receiving the first federated learning related information (“At block 420, requests to participate in training the global machine-learning model may be received from one or more clients,” paragraph 0059 lines 1-3);
verifying whether at least one piece of information contained in the first federated learning related information meets a corresponding federated learning requirement (“A client-selection policy 122 that includes one or more criteria for determining the suitability of the clients 110 for training the global machine-learning model may be stored on the central server 120. The criteria for determining the suitability of a given client 110 for training the global machine-learning model may relate to one or more measurements of the relevance of the local dataset of the given client 110 to the training tasks associated with the global machine-learning model and/or one or more measurements of the capability of the given client 110 to perform the training tasks associated with the global machine-learning model,” paragraph 0029 lines 1-12) through blockchain (“the requests to participate in the training of the global machine-learning model may be published to the blockchain such that a central server and/or other clients may review the requests and identify which clients may participate in the training of the global machine-learning model,” paragraph 0059 lines 8-13), and confirming that the federated learning node is able to participate in federated learning (“A group of clients may be selected as qualified clients to participate in the training of the global machine-learning model based on the requests to participate,” paragraph 0059 lines 13-16), in response to the at least one piece of information contained in the first federated learning related information meeting the corresponding federated learning requirement (“A client-selection policy 122 that includes one or more criteria for determining the suitability of the clients 110 for training the global machine-learning model may be stored on the central server 120. The criteria for determining the suitability of a given client 110 for training the global machine-learning model may relate to one or more measurements of the relevance of the local dataset of the given client 110 to the training tasks associated with the global machine-learning model and/or one or more measurements of the capability of the given client 110 to perform the training tasks associated with the global machine-learning model,” paragraph 0029 lines 1-12);
notifying the federated learning side of indication information indicating federated learning participant nodes that are able to participate in federated learning (“A group of clients may be selected as qualified clients to participate in the training of the global machine-learning model based on the requests to participate,” paragraph 0059 lines 13-16), by the blockchain side (“the requests to participate in the training of the global machine-learning model may be published to the blockchain such that a central server and/or other clients may review the requests and identify which clients may participate in the training of the global machine-learning model,” paragraph 0059 lines 8-13);
determining, by the federated learning side, federated learning participant nodes based on the indication information (“A group of clients may be selected as qualified clients to participate in the training of the global machine-learning model based on the requests to participate,” paragraph 0059 lines 13-16), and
performing data processing by the federated learning nodes on the federated learning side that able to participate in federated learning based on federated learning (“At block 430, one or more local model updates may be obtained from the clients participating in the training of the global machine-learning model,” paragraph 0060 lines 1-3);
wherein the at least one information comprises at least one of identity information, data metadata information, model parameter information and model metadata information of the federated learning node (“The blockchain 130 may include metadata that describes the global machine-learning model of the central server 120, the clients 110, and/or the local datasets of the clients 110. For example, the metadata published to the blockchain 130 may include one or more metadata fields, such as a training task identifier, a training round identifier, a client identifier, a number of training samples (e.g., data entries) included in a given local dataset, a training accuracy of a locally trained machine-learning model and/or a global machine-learning model, a testing accuracy of a locally trained machine-learning model and/or a global machine-learning model, a file name of the global machine-learning model, a threshold number of clients required for a given training round, a threshold training accuracy required for a given training round, etc.,” paragraph 0033 lines 1-15), wherein in response to identity verification of the federated learning node passing, and at least one of data metadata information, model parameter information and model metadata information of the federated learning node meeting the corresponding federated learning requirement, the federated learning node is able to participate in federated learning (“A client-selection policy 122 that includes one or more criteria for determining the suitability of the clients 110 for training the global machine-learning model may be stored on the central server 120. The criteria for determining the suitability of a given client 110 for training the global machine-learning model may relate to one or more measurements of the relevance of the local dataset of the given client 110 to the training tasks associated with the global machine-learning model and/or one or more measurements of the capability of the given client 110 to perform the training tasks associated with the global machine-learning model,” paragraph 0029 lines 1-12);
wherein, the data metadata information includes at least one of data attribute information, data structure information and data distribution information (“A client-selection policy 122 that includes one or more criteria for determining the suitability of the clients 110 for training the global machine-learning model may be stored on the central server 120. The criteria for determining the suitability of a given client 110 for training the global machine-learning model may relate to one or more measurements of the relevance of the local dataset of the given client 110 to the training tasks associated with the global machine-learning model and/or one or more measurements of the capability of the given client 110 to perform the training tasks associated with the global machine-learning model,” paragraph 0029 lines 1-12);
the model parameter information includes at least one of model type, model weight and model gradient (“the clients 110 may publish metadata to the blockchain 130 in which the published metadata describes the local datasets of the clients 110 and/or the clients 110 themselves,” paragraph 0020 lines 3-6); and
wherein, the model metadata information includes at least one of an identifier of a federated learning participant node, a model type, the amount of local training samples, local model training accuracy, and the federated learning participation status information (“A client-selection policy 122 that includes one or more criteria for determining the suitability of the clients 110 for training the global machine-learning model may be stored on the central server 120. The criteria for determining the suitability of a given client 110 for training the global machine-learning model may relate to one or more measurements of the relevance of the local dataset of the given client 110 to the training tasks associated with the global machine-learning model and/or one or more measurements of the capability of the given client 110 to perform the training tasks associated with the global machine-learning model,” paragraph 0029 lines 1-12).
As to dependent claim 26, Zhang further discloses a method comprising:
transmitting third (“one or more training rounds of the global machine-learning model,” paragraph 0014 lines 8-9) federated learning related information by a federated learning node on the federated learning side to the blockchain side (“the requests to participate in the training of the global machine-learning model may be published to the blockchain such that a central server and/or other clients may review the requests and identify which clients may participate in the training of the global machine-learning model,” paragraph 0059 lines 8-13),
verifying, by the blockchain side, whether the federated learning is allowed to initiate based on the third federated learning related information, and transmitting second federated learning related information to the federated learning side if the federated learning is allowed to initiate (“A client-selection policy 122 that includes one or more criteria for determining the suitability of the clients 110 for training the global machine-learning model may be stored on the central server 120. The criteria for determining the suitability of a given client 110 for training the global machine-learning model may relate to one or more measurements of the relevance of the local dataset of the given client 110 to the training tasks associated with the global machine-learning model and/or one or more measurements of the capability of the given client 110 to perform the training tasks associated with the global machine-learning model,” paragraph 0029 lines 1-12), wherein the second federated learning related information is at least a part of the third federated learning related information (“one or more training rounds of the global machine-learning model,” paragraph 0014 lines 8-9),
judging, by each federated learning node on the federated learning side, whether the federated learning node intends to participate in federated learning based on the second federated learning related information (“In response to one or more of the clients 204 determining that its respective local dataset is relevant to performing the training tasks, the clients 204 including relevant local datasets may request to participate in training the global-machine learning model at operations 214,” paragraph 0042 lines 1-5).
As to dependent claim 27, Zhang further discloses a method comprising:
transmitting model parameters of each federated learning node that is allowed to participate in federated learning by the federated learning side (“At block 430, one or more local model updates may be obtained from the clients participating in the training of the global machine-learning model,” paragraph 0060 lines 1-3) to the blockchain side (“At operations 316, metadata of the masked local model updates may be published to the blockchain 306,” paragraph 0053 lines 1-2),
generating a model block by the blockchain side based on the model parameters of each federated learning node (“At block 440, the local model updates obtained by the central server may be aggregated as an aggregated local model update as described in relation to FIG. 2. At block 450, the global machine-learning model may be updated based on the aggregated local model update as described in relation to FIG. 2,” paragraph 0061 lines 1-3),
notifying the federated learning side of the generation of the model block by the blockchain side (“At operations 208, metadata associated with the global machine-learning model of the central server 202 may be published to the blockchain 206,” paragraph 0039 lines 1-3); and
performing local model optimization by each federated learning node on the federated learning side based on the model block (“The central server 202 may send data associated with the global machine-learning model to the clients 204 capable of performing the training tasks at operations 216 such that the respective local dataset corresponding to each of the clients 204 may be used to train the global machine-learning model at operations 214,” paragraph 0042 lines 5-11),
wherein the model block comprises one of sub-model blocks and a global model block generated based on the sub model blocks (“At block 440, the local model updates obtained by the central server may be aggregated as an aggregated local model update as described in relation to FIG. 2. At block 450, the global machine-learning model may be updated based on the aggregated local model update as described in relation to FIG. 2,” paragraph 0061 lines 1-3).
As to dependent claim 29, Zhang further discloses a method comprising:
generating global model parameters on the federated learning side based on model parameters of federated learning nodes on the federated learning side that are allowed to participate in federated learning (“At block 440, the local model updates obtained by the central server may be aggregated as an aggregated local model update as described in relation to FIG. 2. At block 450, the global machine-learning model may be updated based on the aggregated local model update as described in relation to FIG. 2,” paragraph 0061 lines 1-3), and
performing local model optimization by each federated learning node on the federated learning side based on the global model parameters (“The central server 202 may send data associated with the global machine-learning model to the clients 204 capable of performing the training tasks at operations 216 such that the respective local dataset corresponding to each of the clients 204 may be used to train the global machine-learning model at operations 214,” paragraph 0042 lines 5-11).
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure:
US 2019/0188787 A1 disclosing federated learning through blockchain
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/Ryan Barrett/
Primary Examiner, Art Unit 2148
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