Rhychee-FL: Robust and Efficient Hyperdimensional Federated Learning with Homomorphic Encryption

Federated learning (FL) is a widely-used collaborative learning approach where clients train models locally without sharing their data with servers. However, privacy concerns remain since clients still upload locally trained models, which could reveal sensitive information. Fully homomorphic encryption (FHE) addresses this issue by enabling clients to share encrypted models and the server to aggregate them without decryption. While FHE resolves the privacy concerns, the encrypted data introduces larger communication and computational complexity. Moreover, ciphertexts are vulnerable to channel noise, where a single bit error can disrupt model convergence. To overcome these limitations, we introduce Rhychee-FL, the first lightweight and noise-resilient FHE-enabled FL framework based on Hyperdimensional Computing (HDC), a low-overhead training method. Rhychee-FL leverages HDC's small model size and noise resilience to reduce communication overhead and enhance model robustness without sacrificing accuracy or privacy. Additionally, we thoroughly investigate the parameter space of Rhychee-FL and propose an optimized system in terms of computation and communication costs. Finally, we show that our global model can successfully converge without being impacted by channel noise. Rhychee-FL achieves comparable final accuracy to CNN, while reaching 90% accuracy in 6 x fewer rounds and with 2.2 x greater communication efficiency. Our framework shows at least 4.5 x faster client side latency compared to previous FHE-based FL works.