Cooperation and Personalization on a Seesaw: Choice-based FL for Safe Cooperation in Wireless Networks

TL;DR

This paper introduces a choice-based federated learning framework for wireless networks that enhances safety and fairness by allowing participants to adjust their cooperation levels based on trust and safety considerations.

Contribution

It proposes a novel flexible FL framework that enables participants to tune their cooperation, addressing safety and fairness concerns in wireless network applications.

Findings

Framework allows dynamic cooperation adjustment

Enhances safety and fairness in federated learning

Protects participants from malicious attacks

Abstract

Federated learning (FL) is an innovative distributed artificial intelligence (AI) technique. It has been used for interdisciplinary studies in different fields such as healthcare, marketing and finance. However the application of FL in wireless networks is still in its infancy. In this work, we first overview benefits and concerns when applying FL to wireless networks. Next, we provide a new perspective on existing personalized FL frameworks by analyzing the relationship between cooperation and personalization in these frameworks. Additionally, we discuss the possibility of tuning the cooperation level with a choice-based approach. Our choice-based FL approach is a flexible and safe FL framework that allows participants to lower the level of cooperation when they feel unsafe or unable to benefit from the cooperation. In this way, the choice-based FL framework aims to address the safety and fairness concerns in FL and protect participants from malicious attacks.