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FedBit: Accelerating Privacy-Preserving Federated Learning via Bit-Interleaved Packing and Cross-Layer Co-Design
Xiangchen Meng, Yangdi Lyu
https://arxiv.org/abs/2509.23091 http…

FedBit: Accelerating Privacy-Preserving Federated Learning via Bit-Interleaved Packing and Cross-Layer Co-Design
Federated learning (FL) with fully homomorphic encryption (FHE) effectively safeguards data privacy during model aggregation by encrypting local model updates before transmission, mitigating threats from untrusted servers or eavesdroppers in transmission. However, the computational burden and ciphertext expansion associated with homomorphic encryption can significantly increase resource and communication overhead. To address these challenges, we propose FedBit, a hardware/software co-designed f…