提出可自主调节协作程度的联邦学习框架，提升无线网络中的安全与公平性。
Cooperation and Personalization on a Seesaw: Choice-based FL for Safe Cooperation in Wireless Networks
- 通过选择机制动态调整参与方的协作水平，实现灵活安全的联邦学习。
- 在不降低整体性能前提下，允许参与者在不安全时降低协作程度。
- 适合关注隐私保护与对抗恶意攻击的无线网络应用开发者。
联邦学习（FL）是一种创新的分布式人工智能技术，已在医疗、营销和金融等领域开展跨学科研究。然而，其在无线网络中的应用仍处于初期阶段。本文首先综述了将FL应用于无线网络的优势与潜在问题。接着，从协作与个性化的关系出发，分析现有个性化联邦学习框架的特性。此外，探讨了通过选择机制调节协作水平的可能性。所提出的基于选择的联邦学习方法是一种灵活且安全的框架，使参与者在感到不安全或无法从中获益时，可主动降低协作程度。该框架旨在解决联邦学习中的安全与公平性问题，有效防范恶意攻击对参与方的威胁。
原文摘要 · Abstract (English)
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.
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