Enhancing Privacy in Generative AI-Enabled Consumer Electronics Using Homomorphic Encryption and Federated Learning

The integration of Generative AI (GAI) into consumer electronics (e.g., smart homes, wearables) introduces critical privacy risks as sensitive user data fuels personalized services. This paper proposes a homomorphic encryption-federated learning (HE-FL) framework that ensures end-to-end data confidentiality and decentralized model training. By combining HE’s encrypted computation with FL’s distributed architecture, the framework mitigates vulnerabilities in centralized systems while resisting probabilistic polynomial-time adversaries under the Learning With Errors (LWE) assumption. Evaluations on MNIST demonstrate a 3% accuracy trade-off (95.5% vs. 98.5% baseline) for robust privacy, reducing gradient inversion success to ≤5%. Case studies in healthcare wearables and smart grids validate QoS-aware risk mitigation. Challenges in scalability and quantum-era security are addressed through edge-assisted optimizations and hybrid architectures, aligning with GDPR/CCPA compliance to foster trust in GAI-driven ecosystems.