Patent ID: 11941520
Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 19:
20. A computer program product for recommending hyperparameters for a differentially private federated learning process, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to:
iteratively train, by the processor, an overall machine learning model of a differentially private federated learning process, wherein the training comprises, at each iteration:
determine, by the processor, respective values of a hyperparameter for machine learning models distributed on computing devices based on respective privacy budgets, respective learning rate schedules, and respective batch sizes associated with the machine learning models, wherein the respective values of the hyperparameter indicate respective amounts of noise to introduce to respective derivatives of the machine learning models from training to achieve respective defined amounts of privacy of respective training data employed for the training of the machine learning models;
transmit, by the processor, the respective values of the hyperparameter to the computing devices to train the machine learning models and introduce the respective amounts of noise to the respective derivatives of the machine learning models;
receive, by the processor, the respective derivatives of the machine learning models from the computing devices; and
aggregate, by the processor, the respective derivatives of the machine learning models to update the overall machine learning model, wherein the respective derivatives comprise at least model weights.