DVFL-IIoT: Dynamic, Verifiable, and Decentralized Federated Learning with Key Insulation for Industrial Internet of Things

Abstract

Abstract Industrial Internet of Things (IIoT) devices continuously generate large volumes of privacy-sensitive operational data. Federated Learning (FL) enables distributed model training without exposing raw data to external parties. However, existing FL solutions suffer from critical limitations, including single points of failure from centralized servers, insufficient verifiable defenses against gradient poisoning attacks, and poor adaptability to dynamic device churn. To address these challenges, we propose DVFL-IIoT, a fully decentralized and dynamic secure aggregation protocol tailored for IIoT environments. Our framework eliminates centralized trust assumptions using Pedersen Distributed Key Generation (DKG), supports seamless device joining and leaving without full system reinitialization through a key insulation mechanism, and ensures end-to-end verifiability via dual non-interactive zero-knowledge proofs (NIZKs). Formal security analysis proves that DVFL-IIoT achieves IND-CCA2 privacy, information-theoretic collusion resistance, and computational verifiability. Extensive experiments on two real-world IIoT intrusion detection benchmarks, ToN-IoT and Edge-IIoTset, achieve test accuracies of 98.81\% and 98.35\%, respectively, significantly outperforming state-of-the-art methods while maintaining strong robustness against poisoning attacks and dynamic device churn.