Patent ID: 11907403
Assignee: HONG KONG APPLIED SCIENCE AND TECHNOLOGY RESEARCH INSTITUTE CO., LTD
Field: Computer technology (Electrical engineering)
Classification: CPC G  H | IPC G  H

Claim 9:
10. A method of applying dynamic differential privacy enhancements to federated learning, comprising:
receiving model data corresponding to a first data owner, wherein the model data are produced from a machine learning model generated by the first data owner based on noisy data corresponding to the first data owner, wherein the noisy data corresponding to the first data owner comprises a first set of input data with noise corresponding to and satisfying a first privacy loss requirement introduced, and wherein the model data corresponding to the first data owner include noise corresponding to and satisfying a second privacy loss requirement introduced;
receiving model data corresponding to one or more other data owners, wherein the model data corresponding to one or more other data owners are produced from machine learning models generated by the one or more other data owners based on noisy data corresponding to the one or more other data owners, wherein the noisy data corresponding to the one or more other data owners comprises one or more second sets of input data with noise corresponding to and satisfying the first privacy loss requirement introduced, and wherein the model data corresponding to the one or more other data owners include noise corresponding to and satisfying the second privacy loss requirement introduced;
introducing noise corresponding to and satisfying a third privacy loss requirement to the received model data corresponding to the first data owner and to the one or more other data owners;
aggregating received model data corresponding to the first data owner and to the one or more other data owners; and
distributing the aggregated model data to at least one of the first data owner and the one or more other data owners to facilitate subsequent modelling iterations of the machine learning models corresponding to the first data owner and the one or more other data owners.