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.

翻译：联邦学习（FL）是一种创新的分布式人工智能（AI）技术，已在医疗健康、市场营销和金融等不同领域的跨学科研究中得到应用。然而，FL在无线网络中的应用仍处于起步阶段。本文首先概述了将FL应用于无线网络时的优势与潜在问题。接着，通过分析现有个性化FL框架中合作与个性化之间的关系，我们对这些框架提出了新的视角。此外，我们探讨了通过基于选择的方法调整合作水平的可能性。我们提出的基于选择的FL方法是一种灵活且安全的FL框架，允许参与者在感到不安全或无法从合作中获益时降低合作水平。通过这种方式，基于选择的FL框架旨在解决FL中的安全性与公平性问题，并保护参与者免受恶意攻击。