关键词: 联邦学习, 稀疏三元压缩, 通信效率, 同态加密, 隐私保护

Abstract: A federated learning scheme that balances efficiency and security has been designed to address the issue that implementing privacy protection mechanisms in federated learning will increase the communication burden on the system, and sacrificing model accuracy when attempting to improve system communication efficiency. This scheme aims to balance the communication efficiency and privacy security of the model, which is composed of time complexity and communication overhead. Firstly, the parameter sparsification strategy of the sparse ternary compression (STC) algorithm is used to compress the model parameters to be uploaded into triples to reduce the amount of data transmission; then, in order to compensate for the information loss caused by parameter compression, the error feedback mechanism is used to transmit the error as feedback information back to the model training; finally, the Paillier homomorphic encryption technology is used to ensure the privacy security of parameter transmission and aggregation under the premise of efficient model communication. The proposed scheme is compared with the current cutting-edge algorithms in Independent and Identically Distributed(IID) and Non Independent and Identically Distributed(Non-IID) data scenarios on multiple public datasets. The experimental results show that the proposed scheme solves the existing problem of being unable to strike a balance between time complexity, communication overhead and privacy protection, and has extremely high accuracy, convergence and robustness under three mainstream datasets and data distribution conditions.

Key words: federated learning, Sparse Ternary Compression(STC), communication efficiency, homomorphic encryption, privacy protection