PFED-AGG: A Personalized Private Federated Learning Aggregation Algorithm

Yongjie Zhu, Yukun Yan, Qilong Han

Abstract: Federated learning is a special kind of distributed machine learning, in which multiple clients work together to solve a machine learning problem with the collaboration of a central server, and the clients only need to upload parameters for server aggregation instead of uploading raw data, so the privacy of the clients can be protected. However, existing research shows that an attacker who obtains the parameters uploaded by the client can reverse the privacy information of the client, and Federated Learning applies a local differential privacy approach to protect the information of the parameters uploaded by the client from being leaked. However, this privacy protection approach assumes the same level of privacy protection for all clients. To the best of our knowledge, there needs to be work that satisfies the personalized privacy needs of clients. To address this problem, we propose a personalized local differential privacy-based federation framework that satisfies the personalized privacy needs of clients and better protects the privacy of clients by making the specific privacy needs of clients inaccessible to the server. We have conducted extensive experiments on six benchmark datasets, and our approach works better and achieves personalized privacy protection compared to the same privacy-preserving method.

External IDs:dblp:conf/ijcnn/ZhuYH23