TEVA: Training-Efficient and Verifiable Aggregation for Federated Learning for Consumer Electronics in Industry 5.0
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Transactions on Consumer Electronics"
DOI: 10.1109/tce.2024.3517739
Abstract: Federated learning (FL) has been widely used for privacy-preserving model updates in Industry 5.0, facilitated by 6G networks. Despite FL’s privacy-preserving advantages, it remains vulnerable to attacks where adversaries can infer private data from local… read more here.
Keywords: verifiable aggregation; privacy; training efficient; federated learning ... See more keywords
Malicious-Resistant Non-Interactive Verifiable Aggregation for Federated Learning
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Transactions on Dependable and Secure Computing"
DOI: 10.1109/tdsc.2024.3380669
Abstract: In cross-device federated learning, verifiable secure aggregation enables clients to aggregate their locally trained model parameters through a malicious server to obtain a global model. To prevent the malicious server from tampering with the results,… read more here.
Keywords: verifiable aggregation; non interactive; cross device; federated learning ... See more keywords