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

Han Zhang, Medhat Elsayed, Majid Bavand, Raimundas Gaigalas, Yigit Ozcan, Melike Erol-Kantarci

TL;DR

The choice-based FL framework aims to address the safety and fairness concerns in FL and protect participants from malicious attacks and allows participants to lower the level of cooperation when they feel unsafe or unable to benefit from the cooperation.

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.

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

TL;DR

The choice-based FL framework aims to address the safety and fairness concerns in FL and protect participants from malicious attacks and allows participants to lower the level of cooperation when they feel unsafe or unable to benefit from the cooperation.

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.

Paper Structure

This paper contains 8 sections, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. Benefits and Concerns of Federated Learning in Wireless Networks
  3. Benefits of Federated Learning
  4. Concerns about Federated Learning
  5. Tunable Cooperation in Personalized Federated Learning
  6. Choice-based Tunable Federated Learning Framework
  7. Case Study on Cell Sleep Control
  8. Conclusion

Figures (5)

  • Figure 1: The examples of tunable cooperation in FL and their applications to FL-based wireless networks. The left part shows the applications and vulnerabilities of FL in wireless networks. The right part shows examples of tunable cooperation in existing personalized FL techniques. By changing the parameters related to the cooperation degree, given personalized FL techniques will convert between fully cooperative FL and fully personalized independent learning.
  • Figure 2: The pipeline of the proposed choice-based FL.
  • Figure 3: The system model of knowledge distillation-enabled FL-based cell sleep control.
  • Figure 4: System total throughput, average energy efficiency and cooperation level under different numbers of attackers.
  • Figure 5: The cooperation level and system risk level under different numbers of attackers.