DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08/28/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claim 1-14 are objected to because of the following informalities:
Claim 1 recites “the second set of compression parameters” (line 15), which lacks antecedent basis. It appears that this limitation should read “the plurality of second compressed vectors”.
Claim 5 “wherein determining, by the server computing system as the one or more compression error values, the norm of the second aggregate of the plurality of second compressed vectors” on lines 1-3, which lacks antecedent basis. It appears that claim 5 should depend on claim 4.
Claims 2-13 inherit the objection to claim 1 because they do not cure the deficiencies of claim 1.
Claim 14 recites “a estimated mean” on line 16, which appears to be a typo for “an estimated mean”.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-17 are rejected under 35 U.S.C. 101 because the claimed invention recites a judicial exception, is directed to that judicial exception, an abstract idea, as it has not been integrated into a practical application and the claims do not recite significantly more than the judicial exception. The examiner has evaluated the claims under the framework provided in the 2019 Patent Eligibility Guidance published in the Federal Register 01/07/2019 and has provided such analysis below.
Step 1:
Claims 1-14 are directed to methods and fall within the statutory category of processes and claims 15-17 are directed a client computing device and fall within the statutory category of machines. Therefore, “Are the claims to a process, machine, manufacture, or composition of matter?” Yes.
In order to evaluate the Step 2A inquiry “Is the claim directed to a law of nature, a natural phenomenon, or an abstract idea?” we must determine, at Step 2A Prong 1, whether the claim recites a law of nature, a natural phenomenon, or an abstract idea and further whether the claim recites additional elements that integrate the judicial exception into a practical application.
Step 2A Prong 1:
Claims 1 and 14: The limitation “for each of one or more update iterations”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, a person can think and observe, judge and evaluate a set of steps and mentally iterate, with or without the use of pen and paper, the set of steps. The limitation “performing … a global update to the global version of the machine learning model based at least in part on a first aggregate of the plurality of first compressed vectors” (claim 1), as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, a person can think and observe, judge and evaluate the first aggregate of the plurality of first compressed vectors and mentally perform, with or without the use of pen and paper, a global update to the global version of the machine learning model. The limitation “determining … a estimated mean of the data vectors of the client computing devices based at least in part on a first aggregate of the plurality of first compressed vectors” (claim 14), as drafted, is a mathematical calculation. The limitation “determining … one or more compression error values based at least in part on a second aggregate of the plurality of second compressed vectors”, as drafted, is a mathematical calculation (this can be a mathematical calculation OR a mental process). The limitation “updating … the current set of adaptive compression parameters based at least in part on the one or more compression error values”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, a person can think and observe, judge and evaluate one or more compression error values and mentally update, with or without the use of pen and paper, the current set of adaptive compression parameters.
Therefore, yes, claims 1 and 14 recite judicial exceptions.
Step 2A Prong 2:
Claims 1 and 14: The judicial exception is not integrated into a practical application. In particular, the claims recite the following additional element--“a server computing system”, which is merely a recitation of generic computing components and functions being used as a tool to apply the abstract idea (see MPEP § 2106.05(f)), which does not integrate a judicial exception into a practical application. Further, the claims recite “communicating … a current set of adaptive compression parameters to a plurality of client computing devices”, “receiving … a plurality of first compressed vectors respectively from the plurality of client computing devices”, and “receiving … a plurality of second compressed vectors respectively from the plurality of client computing devices”, which are merely insignificant data gathering activities (see MPEP § 2106.05(g)) which does not integrate a judicial exception into a practical application and is also well-understood, routine, and conventional (see MPEP § 2106.05(d)(II): “The courts have recognized the following computer functions as well-understood, routing, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of granularity) or as insignificant extra-solution activity (i. Receiving or transmitting data over a network, e.g., using the Internet to gather data)”. That is, in the instant claims, these limitations merely receive or transmit/provide data which is well-understood, routine, and conventional. Lastly, the claims recite “wherein the first compressed vector received from each client computing device has been generated by performance by the client computing device of a compression algorithm to a respective update vector of the client computing device using the current set of adaptive compression parameters” and “wherein the second compressed vector received from each client computing device has been generated by application by the client computing device of the compression algorithm to the respective update vector of the client computing device using the second set of compression parameters”, which are merely recitations of field of use/technological environment (see MPEP § 2106.05(h)) which do not integrate a judicial exception into a practical application.
Therefore, “Do the claims recites additional elements that integrate the judicial exception into a practical application?” No, these additional elements do not integrate the abstract idea into a practical application and they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
After evaluating the inquiries set forth in Steps 2A Prongs 1 and 2, it has been concluded that claims 1 and 14 not only recite a judicial exception but that the claims are directed to the judicial exception as the judicial exception has not been integrated into a practical application.
Step 2B:
Claims 1 and 14: The claims do not recite additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than generic computing components and mere instructions to apply an exception and field of use/technological environment which do not amount to significantly more than the abstract idea.
Therefore, “Do the claims recite additional elements that amount to significantly more than the judicial exception?” No, these additional elements, alone or in combination, do not amount to significantly more than the judicial exception.
Having concluded analysis within the provided framework, claims 1 and 14 do not recite patent eligible subject matter under 35 USC 101.
With regard to claim 2, it recites the additional elements of “the current set of adaptive compression parameters has a first compression rate”, “the second set of adaptive compression parameters has a second compression rate”, and “the first compression rate is smaller than the second compression rate”, which are merely recitations of field of use/technological environment (see MPEP § 2106.05(h)) which does not integrate a judicial exception into a practical application. Further, claim 2 does not recite any further additional elements and for the same reasons as above with regard to integration into a practical application and whether additional elements amount to significantly more, claim 2 fails both Step 2A Prong 2 for being directed to a judicial exception that has not been integrated into a practical application and Step 2B for not amounting to significantly more. Therefore, claim 2 does not recite patent eligible subject matter under 35 USC 101.
With regard to claim 3, the limitation “when the one or more compression error values indicate compression error greater than a desired compression error: updating, by the server computing system, the current set of adaptive compression parameters so as to decrease a first compression rate associated with the current set of adaptive compression parameters”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, a person can think and observe, judge and evaluate when the one or more compression error values indicate compression error greater than a desired compression error and mentally update, with or without the use of pen and paper, the current set of adaptive compression parameters so as to decrease a first compression rate associated with the current set of adaptive compression parameters. Further, the limitation “when the one or more compression error values indicate compression error less than the desired compression error: updating, by the server computing system, the current set of adaptive compression parameters so as to increase the first compression rate associated with the current set of adaptive compression parameters”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, a person can think and observe, judge and evaluate when the one or more compression error values indicate compression error less than the desired compression error and mentally update, with or without the use of pen and paper, the current set of adaptive compression parameters so as to increase the first compression rate associated with the current set of adaptive compression parameters. Further, claim 3 does not recite any further additional elements and for the same reasons as above with regard to integration into a practical application and whether additional elements amount to significantly more, claim 3 fails both Step 2A Prong 2 for being directed to a judicial exception that has not been integrated into a practical application and Step 2B for not amounting to significantly more. Therefore, claim 3 does not recite patent eligible subject matter under 35 USC 101.
With regard to claim 4, the limitation “determining, by the server computing system as the one or more compression error values, a norm of the second aggregate of the plurality of second compressed vectors”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, a person can think and observe, judge and evaluate the second aggregate of the plurality of second compressed vectors and mentally determine, with or without the use of pen and paper, its norm. Further, claim 4 does not recite any further additional elements and for the same reasons as above with regard to integration into a practical application and whether additional elements amount to significantly more, claim 4 fails both Step 2A Prong 2 for being directed to a judicial exception that has not been integrated into a practical application and Step 2B for not amounting to significantly more. Therefore, claim 4 does not recite patent eligible subject matter under 35 USC 101.
With regard to claim 5, the limitation “determining, by the server computing system as the one or more compression error values, a differentially private norm of the second aggregate of the plurality of second compressed vectors”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, a person can think and observe, judge and evaluate the second aggregate of the plurality of second compressed vectors and mentally determine, with or without the use of pen and paper, its differentially private norm. Further, claim 5 does not recite any further additional elements and for the same reasons as above with regard to integration into a practical application and whether additional elements amount to significantly more, claim 5 fails both Step 2A Prong 2 for being directed to a judicial exception that has not been integrated into a practical application and Step 2B for not amounting to significantly more. Therefore, claim 5 does not recite patent eligible subject matter under 35 USC 101.
With regard to claim 6, the limitation “decompressing, by the server computing system, the first aggregate of the plurality of first compressed vectors to obtain an estimated mean”, as drafted, is a mathematical calculation. The limitation “performing, by the server computing system, the global update to the global version of the machine learning model based at least in part on the estimated mean”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, a person can think and observe, judge and evaluate the estimated mean and mentally perform, with or without the use of pen and paper, the global update to the global version of the machine learning model. Further, claim 6 does not recite any further additional elements and for the same reasons as above with regard to integration into a practical application and whether additional elements amount to significantly more, claim 6 fails both Step 2A Prong 2 for being directed to a judicial exception that has not been integrated into a practical application and Step 2B for not amounting to significantly more. Therefore, claim 6 does not recite patent eligible subject matter under 35 USC 101.
With regard to claim 7, the limitations “re-compressing, by the server computing system, the estimated mean according to the second set of compression parameters to obtain a re-compressed aggregate” and “determining, by the server computing system as the one or more compression error values, a difference between the re-compressed aggregate and the second aggregate”, as drafted, are mathematical calculations. Further, claim 7 does not recite any further additional elements and for the same reasons as above with regard to integration into a practical application and whether additional elements amount to significantly more, claim 7 fails both Step 2A Prong 2 for being directed to a judicial exception that has not been integrated into a practical application and Step 2B for not amounting to significantly more. Therefore, claim 7 does not recite patent eligible subject matter under 35 USC 101.
With regard to claim 8, it recites the additional element of “wherein the first aggregate of the plurality of first compressed vectors comprises a differentially private aggregate of the plurality of first compressed vectors”, which is merely a recitation of field of use/technological environment (see MPEP § 2106.05(h)) which does not integrate a judicial exception into a practical application. Further, claim 8 does not recite any further additional elements and for the same reasons as above with regard to integration into a practical application and whether additional elements amount to significantly more, claim 8 fails both Step 2A Prong 2 for being directed to a judicial exception that has not been integrated into a practical application and Step 2B for not amounting to significantly more. Therefore, claim 8 does not recite patent eligible subject matter under 35 USC 101.
With regard to claim 9, it recites the additional element of “wherein the first aggregate of the plurality of first compressed vectors comprises a Secure Aggregation (SecAgg) aggregate of the plurality of first compressed vectors”, which is merely a recitation of field of use/technological environment (see MPEP § 2106.05(h)) which does not integrate a judicial exception into a practical application. Further, claim 9 does not recite any further additional elements and for the same reasons as above with regard to integration into a practical application and whether additional elements amount to significantly more, claim 9 fails both Step 2A Prong 2 for being directed to a judicial exception that has not been integrated into a practical application and Step 2B for not amounting to significantly more. Therefore, claim 9 does not recite patent eligible subject matter under 35 USC 101.
With regard to claim 10, it recites the additional elements of “wherein the current set of adaptive compression parameters comprise one or more sketch sizes for one or more sketching operations” and “wherein the plurality of first compressed vectors comprise sketched vectors generated by application of the one or more sketching operations”, which are merely recitations of field of use/technological environment (see MPEP § 2106.05(h)) which do not integrate a judicial exception into a practical application. Further, claim 10 does not recite any further additional elements and for the same reasons as above with regard to integration into a practical application and whether additional elements amount to significantly more, claim 10 fails both Step 2A Prong 2 for being directed to a judicial exception that has not been integrated into a practical application and Step 2B for not amounting to significantly more. Therefore, claim 10 does not recite patent eligible subject matter under 35 USC 101.
With regard to claim 11, it recites the additional element of “wherein the second set of compression parameters is fixed for the update iterations”, which is merely a recitation of field of use/technological environment (see MPEP § 2106.05(h)) which does not integrate a judicial exception into a practical application. Further, claim 11 does not recite any further additional elements and for the same reasons as above with regard to integration into a practical application and whether additional elements amount to significantly more, claim 11 fails both Step 2A Prong 2 for being directed to a judicial exception that has not been integrated into a practical application and Step 2B for not amounting to significantly more. Therefore, claim 11 does not recite patent eligible subject matter under 35 USC 101.
With regard to claim 12, it recites the additional element of “wherein the respective update vector of each client computing device describes updates to model parameters of a local version of the machine learning model stored at the client computing device that result from training the local version of the machine learning model on local training data stored at the client computing device”, which is merely a recitation of field of use/technological environment (see MPEP § 2106.05(h)) which does not integrate a judicial exception into a practical application. Further, claim 12 does not recite any further additional elements and for the same reasons as above with regard to integration into a practical application and whether additional elements amount to significantly more, claim 12 fails both Step 2A Prong 2 for being directed to a judicial exception that has not been integrated into a practical application and Step 2B for not amounting to significantly more. Therefore, claim 12 does not recite patent eligible subject matter under 35 USC 101.
With regard to claim 13, the limitation “wherein the one or more update iterations comprise a plurality of update iterations”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, a person can think and observe, judge and evaluate a set of steps and mentally iterate, with or without the use of pen and paper, the set of steps a plurality of times. Further, claim 13 does not recite any further additional elements and for the same reasons as above with regard to integration into a practical application and whether additional elements amount to significantly more, claim 13 fails both Step 2A Prong 2 for being directed to a judicial exception that has not been integrated into a practical application and Step 2B for not amounting to significantly more. Therefore, claim 13 does not recite patent eligible subject matter under 35 USC 101.
With regard to claim 15, the limitation “for each of one or more update iterations”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, a person can think and observe, judge and evaluate a set of steps and mentally iterate, with or without the use of pen and paper, the set of steps. The limitations “applying a compression algorithm to the update vector using the current set of adaptive compression parameters to generate a first compressed vector” and “applying the compression algorithm to the update vector using a second set of compression parameters to generate a second compressed vector”, as drafted, are processes that, but for the recitation of generic computing components, under its broadest reasonable interpretation, cover performance of the limitation in the mind. For example, a person can think and observe, judge and evaluate the update vector and mentally apply, with or without the use of pen and paper, a compression algorithm to generate a first/second compressed vector.
Therefore, yes, claim 15 recites judicial exceptions.
The judicial exception is not integrated into a practical application. In particular, the claim recite the following additional elements--“a server computing system” and “training a local version of a machine learning model on local training data stored at the client computing device to generate an update vector that describes updates to model parameters of the local version of the machine learning model stored at the client computing device”, which are merely recitations of generic computing components and/or functions being used as a tool to apply the abstract idea (see MPEP § 2106.05(f)), which does not integrate a judicial exception into a practical application. Further, the claim recites “receiving … a current set of adaptive compression parameters” and “transmitting the first compressed vector and the second compressed vector to the server computing system”, which are merely insignificant data gathering activities (see MPEP § 2106.05(g)) which does not integrate a judicial exception into a practical application and is also well-understood, routine, and conventional (see MPEP § 2106.05(d)(II): “The courts have recognized the following computer functions as well-understood, routing, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of granularity) or as insignificant extra-solution activity (i. Receiving or transmitting data over a network, e.g., using the Internet to gather data)”. That is, in the instant claims, these limitations merely receive or transmit/provide data which is well-understood, routine, and conventional.
Therefore, “Do the claims recites additional elements that integrate the judicial exception into a practical application?” No, these additional elements do not integrate the abstract idea into a practical application and they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
After evaluating the inquiries set forth in Steps 2A Prongs 1 and 2, it has been concluded that claim 15 not only recites a judicial exception but that the claim is directed to the judicial exception as the judicial exception has not been integrated into a practical application.
The claims do not recite additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than generic computing components.
Therefore, “Do the claims recite additional elements that amount to significantly more than the judicial exception?” No, these additional elements, alone or in combination, do not amount to significantly more than the judicial exception.
Having concluded analysis within the provided framework, claim 15 does not recite patent eligible subject matter under 35 USC 101.
With regard to claim 16, the limitation “wherein, at each update iteration, the current set of adaptive compression parameters has been updated based on one or more compression error values generated based at least in part on the second compressed vector transmitted by the client computing device at the prior update iteration”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, a person can think and observe, judge and evaluate one or more compression error values generated based at least in part on the second compressed vector transmitted by the client computing device at the prior update iteration and mentally update, with or without the use of pen and paper, the current set of adaptive compression parameters. Further, claim 16 does not recite any further additional elements and for the same reasons as above with regard to integration into a practical application and whether additional elements amount to significantly more, claim 16 fails both Step 2A Prong 2 for being directed to a judicial exception that has not been integrated into a practical application and Step 2B for not amounting to significantly more. Therefore, claim 16 does not recite patent eligible subject matter under 35 USC 101.
With regard to claim 17, it recites the additional elements of “the current set of adaptive compression parameters has a first compression rate”, “the second set of adaptive compression parameters has a second compression rate”, and “the first compression rate is smaller than the second compression rate”, which are merely recitations of field of use/technological environment (see MPEP § 2106.05(h)) which does not integrate a judicial exception into a practical application. Further, claim 17 does not recite any further additional elements and for the same reasons as above with regard to integration into a practical application and whether additional elements amount to significantly more, claim 17 fails both Step 2A Prong 2 for being directed to a judicial exception that has not been integrated into a practical application and Step 2B for not amounting to significantly more. Therefore, claim 17 does not recite patent eligible subject matter under 35 USC 101.
Therefore, claims 1-17 do not recite patent eligible subject matter under 35 USC 101.
Claim Rejections - 35 USC § 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.
Claim(s) 15-17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Gottin et al. (U.S. Patent Application Publication No. 2024/0028974, hereinafter “Gottin”).
Claim 15:
Gottin discloses a client computing device (§ 0040, Line 5; Client nodes) configured to perform operations, the operations comprising:
for each of one or more update iterations (§ 0040, Lines 3-4; The methodology 100 may operate iteratively, or in cycles):
receiving, from a server computing system, a current set of adaptive compression parameters (§ 0024, Lines 7-10; One example of quantization is data compression, in which a size of a dataset is reduced, in some way, to create a smaller dataset that corresponds to the larger dataset) (§ 0072; Signal the edge nodes for the desired elected quantization level updates to be gathered);
training a local version of a machine learning model on local training data stored at the client computing device to generate an update vector that describes updates to model parameters of the local version of the machine learning model stored at the client computing device (§ 0040, Lines 5-9; The client nodes download the current model from the central node, then each client node may train the model, using local client node data, during a user-defined number of epochs);
applying a compression algorithm to the update vector using the current set of adaptive compression parameters (“quantization function”) to generate a first compressed vector (§ 0058, Lines 3-4; Let “F” be a set of known quantization functions, such as compression functions) (§ 0062, Lines 1-3; For each available gradient compression, or other quantization function, f€F, obtain a model Wf resulting from the updated model W with f(G));
applying the compression algorithm to the update vector using a second set of compression parameters (another “quantization function”) to generate a second compressed vector (See citation above. Model Wf is obtained for each available gradient compression); and
transmitting the first compressed vector and the second compressed vector to the server computing system (§ 0078, Lines 1-4; Example embodiments of the method may perform the collection of statistics from the procedures performed inside the edge nodes so that the central node may select the best quantization procedure).
Claim 16:
Gottin further discloses wherein, at each update iteration, the current set of adaptive compression parameters has been updated based on one or more compression error values generated based at least in part on the second compressed vector transmitted by the client computing device at the prior update iteration (§ 0059, Lines 3-5; The selected edge nodes are configured to estimate and evaluate a convergence rate for a given quantization function) (§ 0071; Elect a compression method that was selected by the majority of edge nodes as achieving an adequate compression/convergence tradeoff) (§ 0076, Lines 11-14; The edge nodes may run various quantization processes and identify which quantization process provides the best performance, for example, based on an estimate convergence rate) (§ 0083, Lines 4-6; The loss experienced by the model W for each different compression method may be obtained).
Claim 17:
Gottin further discloses wherein:
the current set of adaptive compression parameters has a first compression rate (§ 0062, Lines 1-3; For each available gradient compression, or other quantization function, f€F, obtain a model Wf resulting from the updated model W with f(G));
the second set of adaptive compression parameters has a second compression rate (See citation above. Each compression function has a different compression/convergence tradeoff, see § 0071); and
the first compression rate is smaller than the second compression rate (§ 0032, Lines 1-2; Aggressive compression may come at a price, namely, poor model convergence performance) (§ 0047; Deciding when to send (1) a complete 32-bit gradient, which is more informative than a compressed gradient or (2) a quantized version of the gradient(s), which may be less informative than complete gradients, but smaller in size and therefore less intensive in terms of bandwidth consumption).
Examiner’s Note
There is no prior art rejection for claims 1-14.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
U.S. Patent Application Publication No. 2022/0156633 (Anwar et al.) – Adaptive compression in federated learning where a percentage of the most changed parameters are sent to a parameter server as a compressed update.
U.S. Patent Application Publication No. 2022/0383091 (Das et al.) – Vertical federated learning where updated compressed embeddings are obtained from client computing device, the updated compressed embeddings compressed by clustering.
U.S. Patent Application Publication No. 2023/0325652 (Balevi et al.) – Gradient grouping for compression in federated learning for machine learning models where a representative value of all gradients within each gradient grouping is computed and transmitted to the network entity for each round of federated learning.
U.S. Patent Application Publication No. 2024/0174254 (Chen et al.) – Communication-aware federated learning where a level of compression is determined based on the network bandwidth of the channel and the trained machine learning model is compressed based on the determined level of compression.
U.S. Patent Application Publication No. 2022/0335269 (Zhang et al.) – A compression framework for federated learning with predictive compression paradigm where the residual of weight updates and the parameters to calculate the predicted weight update are sent to the central server.
U.S. Patent Application Publication No. 2022/0391778 (Green et al.) – A federated learning framework where a centralized model is trained on training data distributed over a large number of clients each with unreliable network connections and low computational power. Embeddings are generated with local training data while preserving the privacy of a user of the client device.
U.S. Patent Application Publication No. 2023/0082173 (Li et al.) – Federated learning training method where trained model parameters may be compressed model parameters and the compression proportion/ratio may be set based on a specific application scenario.
U.S. Patent Application Publication No. 2023/0237321 (Cirillo et al.) – Privacy-preserving federated machine learning where labeling functions is shared among different parties in order to share the costs to develop them while preserving privacy.
U.S. Patent Application Publication No. 2024/0005215 (Ong et al.) – Training models for federated learning where gradient and hessian data are compressed to reduce overall network transfer overhead and increase federated learning stability and performance in creating machine learning models.
U.S. Patent Application Publication No. 2024/0256973 (Vandikas et al.) – Obtaining an aggregated characteristic of updates to a machine learning model from a first subset of nodes, comparing the aggregated characteristic to an equivalent reference, and identifying whether the first subset of nodes are contributing updates that are corrupting the machine learning model.
U.S. Patent Application Publication No. 2021/0383197 (Da Silva et al.) – Adaptive stochastic learning state compression for federated learning where the adaptive data compressor employ stochastic k-level quantization.
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/NAM T TRAN/Primary Examiner, Art Unit 2455