Blockchain-Enhanced Federated Learning for Secure and Intelligent Consumer Electronics: An Overview
Chenlu Zhu, Shuhuai Wang, Xiaoxuan Fan, Xianjun Deng, Shenghao Liu, Yuanyuan He, Celimuge Wu · IEEE Consumer Electronics Magazine · 2025
With the rapid advancement of artificial intelligence and Internet of Things, consumer electronics have become essential in daily life. However, these devices generate and store vast amounts of personal data, posing significant security threats. Federated learning, a distributed machine learning method, protects user privacy by training models collaboratively without sharing raw data. However, it still faces numerous challenges. To further enhance data security, the integration of blockchain technology is crucial. Blockchain's decentralized and immutable characteristics enable secure data sharing and privacy protection in consumer electronics. This article analyzes the application of federated learning in consumer electronics, examines how blockchain can enhance federated learning, and discusses future research directions. The primary contributions of this article include a detailed application of blockchain-enhanced federated learning in consumer electronics, intended to serve as a reference for future research.