Blockchain-Empowered Resilient Attack Defense in Federated Learning for Consumer Electronics

Artificial intelligence (AI) empowers consumer electronics, making daily life more convenient, but it also introduces potential privacy risks. Governments worldwide have implemented stringent data regulations to protect individual privacy. Federated learning (FL) helps AI comply with these regulations and continuously evolve in consumer electronics. However, FL faces significant structural issues and vulnerabilities to parameter pollution attacks. This article introduces a novel FL approach for consumer electronics AI applications. Our proposal features a blockchain-empowered decentralized trust mechanism combined with fault defenses to protect against malicious nodes in FL. We also utilize knowledge distillation and meta-learning techniques to thwart poisoning attacks, ensuring the integrity of computations in FL for consumer electronics. Experimental results demonstrate that our approach effectively preserves the integrity of FL processes in consumer electronics, even with over 50% of malicious nodes. This highlights the robust structural and computational fault tolerance of the proposed method.