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

Claim 11:
12. A computer-implemented method, comprising:
iteratively training, by a system operatively coupled to a processor, an overall machine learning model of a differentially private federated learning process, wherein the training comprises, at each iteration:
determining respective values of a hyperparameter for machine learning models distributed on computing devices 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;
transmitting 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;
receiving the respective derivatives of the machine learning models from the computing devices; and
aggregating the respective derivatives of the machine learning models to update the overall machine learning model, wherein the respective derivatives comprise at least model weights.