HERL: Tiered Federated Learning with Adaptive Homomorphic Encryption using Reinforcement Learning

Authors: Jiaxang Tang, Zeshan Fayyaz, Mohammad A. Salahuddin, Raouf Boutaba, Zhi-Li Zhang, Ali Anwar
Published: 2024-09-11

Source: https://arxiv.org/abs/2409.07631

PDF: https://arxiv.org/pdf/2409.07631

Labels Predicted by AI

FLとHEの統合 プライバシー保護 通信効率

Please note that these labels were automatically added by AI. Therefore, they may not be entirely accurate.
For more details, please see the About the Literature Database page.

Abstract

Federated Learning is a well-researched approach for collaboratively training machine learning models across decentralized data while preserving privacy. However, integrating Homomorphic Encryption to ensure data confidentiality introduces significant computational and communication overheads, particularly in heterogeneous environments where clients have varying computational capacities and security needs. In this paper, we propose HERL, a Reinforcement Learning-based approach that uses Q-Learning to dynamically optimize encryption parameters, specifically the polynomial modulus degree, N, and the coefficient modulus, q, across different client tiers. Our proposed method involves first profiling and tiering clients according to the chosen clustering approach, followed by dynamically selecting the most suitable encryption parameters using an RL-agent. Experimental results demonstrate that our approach significantly reduces the computational overhead while maintaining utility and a high level of security. Empirical results show that HERL improves utility by 17 convergence efficiency by up to 30