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 framework optimized for the Brakerski-Fan-Vercauteren (BFV) scheme. FedBit employs bitinterleaved data packing to embed multiple model parameters into a single ciphertext coefficient, thereby minimizing ciphertext expansion and maximizing computational parallelism. Additionally, we integrate a dedicated FPGA accelerator to handle cryptographic operations and an optimized dataflow to reduce the memory overhead. Experimental results demonstrate that FedBit achieves a speedup of two orders of magnitude in encryption and lowers average communication overhead by $60.7 \%$, while maintaining high accuracy.