The Internet of Things (IoT) consistently generates vast amounts of data, sparking increasing concern over the protection of data privacy and the limitation of data misuse. Federated learning (FL) facilitates collaborative capabilities among multiple parties by sharing machine learning (ML) model parameters instead of raw user data, and it has recently gained significant attention for its potential in privacy preservation and learning efficiency enhancement. In this paper, we highlight the digital ethics concerns that arise when human-centric devices serve as clients in FL. More specifically, challenges of game dynamics, fairness, incentive, and continuity arise in FL due to differences in perspectives and objectives between clients and the server. We analyze these challenges and their solutions from the perspectives of both the client and the server, and through the viewpoints of centralized and decentralized FL. Finally, we explore the opportunities in FL for human-centric IoT as directions for future development.

翻译：物联网（IoT）持续产生海量数据，引发了对数据隐私保护和数据滥用限制日益增长的关注。联邦学习（FL）通过共享机器学习（ML）模型参数而非原始用户数据，促进了多方协作能力，并因其在隐私保护和提升学习效率方面的潜力而近期备受瞩目。本文聚焦于人本设备作为联邦学习客户端时引发的数字伦理问题。具体而言，由于客户端与服务器在视角和目标上的差异，联邦学习中出现了博弈动态性、公平性、激励机制及持续性问题。我们从客户端与服务端的双重视角，结合中心化与去中心化联邦学习的框架分析这些挑战及其解决方案。最后，我们探讨了联邦学习在人本物联网中的发展机遇，作为未来研究方向的指引。