Efficient Fast Additive Homomorphic Encryption Cryptoprocessor for Privacy-Preserving Federated Learning Aggregation

Privacy leakage is a critical concern of collaboratively training a large-scale deep learning model from multiple clients. To protect the local data, homomorphic encryption (e.g., Paillier) could be utilized for data aggregation on the central server. Nevertheless, even with CPU-optimized libraries or FPGA-based accelerators, the computing power and throughput limitations remain a stumbling block for practical deployment of Paillier scheme. In this paper, we present an efficient and high-throughput cryptoprocessor based on a recently introduced Fast Additive Homomorphic Encryption (FAHE) algorithm. For encryption, we incorporate the asymmetric decomposition, time multiplexing resource reuse and hard-macro based wide-bus logic operations to efficiently map the large (>40 kbits) integer multiplications for low latency FPGA implementation. For decryption, we propose a table lookup method for rapid modular reduction by leveraging the relative short modulus size of FAHE. The single large precomputed lookup table is carefully partitioned into multiple subtables and deployed in dual-port RAMs to enable resource-efficient parallel computation. The FAHE cryptoprocessor is implemented on a Xilinx ZCU102 FPGA board for performance evaluation and comparison. The results show that the throughput of our design is 354 × to 404 × higher than the state-of-the-art Paillier accelerators. Compared to the FAHE software implementation, the latency of our proposed design is 14.95 × and 11.42 × lower for encryption and decryption, respectively.