On Safeguarding Privacy and Security in the Framework of Federated Learning
Chuan Ma, Jun Li, Ming Ding, Howard H. Yang, Feng Shu, Tony Q. S. Quek, H. Vincent Poor
Abstract
Motivated by the advancing computational capacity of wireless end-user equipment (UE), as well as the increasing concerns about sharing private data, a new machine learning (ML) paradigm has emerged, namely federated learning (FL). Specifically, FL allows a decoupling of data provision at UEs and ML model aggregation at a central unit. By training model locally, FL is capable of avoiding direct data leakage from the UEs, thereby preserving privacy and security to some extent. However, even if raw data are not disclosed from UEs, an individual's private information can still be extracted by some recently discovered attacks against the FL architecture. In this work, we analyze the privacy and security issues in FL, and discuss several challenges to preserving privacy and security when designing FL systems. In addition, we provide extensive simulation results to showcase the discussed issues and possible solutions.