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

Claim 7:
8. A system, comprising:
a memory that stores computer executable components; and
a processor, operably coupled to the memory, and that executes the computer executable components stored in the memory, wherein the computer executable components comprise:
a model component that iteratively trains a machine learning model of a differentially private federated learning process, wherein the training comprises, at each iteration:
receiving, from a server device, a hyperparameter that was determined based on a privacy budget, a learning rate schedule, and a batch size associated with the machine learning model, wherein the value of the hyperparameter indicates an amount of noise to introduce to derivatives of the machine learning model from training to achieve a defined amount of privacy of training data employed for the training of the machine learning model, wherein the derivatives comprise at least model weights;
training the machine learning model using the training data;
introducing the amount of noise to the derivatives of the machine learning models from the training; and
sending the derivatives to the server device for training an overall machine learning model of the differentially private federated learning process.