Homomorphic-encryption-based Decentralized Federated Learning

To meet the requirements of the artificial internet of things (AIoT), decentralization is essential to provide wide coverage, trustworthiness, and low-latency communication. For this purpose, decentralized federated learning (DFL) has rapidly evolved and gained popularity in recent years by reducing reliance on a central server and promoting a more robust, scalable, and privacy-preserving system. Nevertheless, the exchange of model updates and gradients in peer-to-peer (P2P) wireless communication systems introduces new vulnerabilities that threaten both model performance and data security, while frequent P2P communication among clients can lead to high communication costs. To address these issues, in this work, we develop a communicationefficient and security-enhanced DFL algorithm, which integrates the fast incremental alternating direction method of multipliers (FI-ADMM) algorithm with parameter-selective additively homomorphic encryption. Additionally, by introducing a first-order approximation for primal updates and rearranging the update order in FI-ADMM, the proposed method is superior to the other benchmarks in terms of computational and time complexity, which is validated by theoretical analysis and simulations.