With the growing need to comply with privacy regulations and respond to user
data deletion requests, integrating machine unlearning into IoT-based federated
learning has become imperative. Traditional unlearning methods, however, often
lack verifiable mechanisms, leading to challenges in establishing trust. This
paper delves into the innovative integration of blockchain technology with
federated learning to surmount these obstacles. Blockchain fortifies the
unlearning process through its inherent qualities of immutability,
transparency, and robust security. It facilitates verifiable certification,
harmonizes security with privacy, and sustains system efficiency. We introduce
a framework that melds blockchain with federated learning, thereby ensuring an
immutable record of unlearning requests and actions. This strategy not only
bolsters the trustworthiness and integrity of the federated learning model but
also adeptly addresses efficiency and security challenges typical in IoT
environments. Our key contributions encompass a certification mechanism for the
unlearning process, the enhancement of data security and privacy, and the
optimization of data management to ensure system responsiveness in IoT
scenarios.

整合区块链技术和联邦学习以应对隐私合规和用户数据删除请求的需求，该研究论文探讨了传统遗忘方法的不足之处，并介绍了一种将区块链与联邦学习融合的框架，以确保遗忘请求和行动的不可变记录，并提升数据安全性和隐私保护，以及优化数据管理以保证物联网环境中的系统响应能力。