AHE: Adaptive Homomorphic Encryption for Customizable Privacy in Heterogeneous Federated Learning

Abstract

Homomorphic Encryption (HE) serves as a vital privacy-preserving technique in federated learning (FL) applications. However, while HE offers substantial privacy benefits, it also introduces significant computational and communication overhead. This challenge is further compounded in real-world heterogeneous scenarios, where clients vary in computing resources and privacy requirements. Existing solutions apply uniform static HE configuration to satisfy the privacy requirements of the most stringent client, resulting in high resource usage for all the clients. We propose AHE, a novel FL framework that integrates HE with adaptive privacy-aware clustering to dynamically optimize the latency-accuracy-privacy trade-off. AHE introduces two key innovations: (1) adaptive HE parameter management, which tailors encryption levels to individual client capabilities and customizable privacy needs, and (2) private cross-cluster aggregation, a lightweight key-switching based protocol that privately combines model updates across clusters with divergent HE schemes. To further minimize overhead, we design a parallelized key-switching mechanism that reduces server-side latency. Experimental results demonstrate that our approach enhances various FL applications by improving final accuracy up to 19% and reducing global round time up to 70%, significantly boosting training efficiency. Additionally, AHE is robust to dynamic client shift, and the parallelization strategy dramatically reduces server-side latency, enabling better scalability.