The Conceptual Framework of a Privacy-Aware Federated Data Collecting and Learning System

Abstract

The federated learning methods offer a strong background of fusing and publishing simultaneously collected data. One of the most challenging problems in federated learning is to hide the identity of the participants. Privacy-preserving techniques try to perturb the participants' local data to match its distribution to the global data.In this paper, we consider that agents collect local environmental data. Neighboring agents can share some of their raw data to support real-time decisions and reduce deviation from the global data distribution. The agents will fuse their collected data into a global model that supports the long-term decision and plan making.We assume the specific situation where the necessary communication protocol between the agents may lead to sharing too much local raw data uncovering private and sensitive attributes of the data sharers.To handle privacy issues, we introduce a privacy-aware framework. Within this framework, local participants balance the amount of the shared raw data to make it informative enough yet not revealing, effectively bounding the loss of privacy. In this study, we use autonomous vehicle agents as an example to demonstrate the concepts of the proposed framework.

Towards an Efficient, Privacy-aware Federated Learning Scheme

“…Moreover, FL provides a theoretical defense against active cyberattacks, but an honest-but-curious eavesdropper can easily carry out a membership inference attack if the participants' data is not independent and identically distributed (non-IID) [6]. To this end, in this paper, we provide a proof-ofconcept experiment of a novel privacy-preserving FL scheme based on statistical anonymity achieved by a small amount of raw data sharing [7].…”