Federated Projection Hamming Hash Latent Factor Learning Model

Federated Learning (FL) offers a means to train models collaboratively across decentralized data sources while preserving privacy. However, prevalent real-valued FL models face challenges with high computational and communication costs, along with potential privacy breaches. Hash Learning, by converting real values to binary codes, presents a solution but is traditionally limited by its lack of privacy considerations and failure to account for the varying importance of bit positions, resulting in information loss. We propose the, Federated Projection-Hamming Hash Latent Factor (FPHLF) model, which addresses three key challenges in traditional FL: computational costs, storage overhead, and privacy risks. FPHLF innovates by a) Utilizing Projected Hamming Distance to enhance binary code representation by recognizing bit significance; b) Implementing efficient bitwise operations to minimize FL's computational and storage demands; and c) Opting for binary update direction matrices over real-valued gradients to reduce communication costs and strengthen privacy. FPHLF achieves high accuracy with reduced computational, storage, and communication expenses, and mitigates privacy risks. Experiments on four real-world datasets show FPHLF outperforms existing hashing and FL methods in accuracy, efficiency, and security. The source code, accessible at https://github.com/LiCaCO3/FPHLF.