Compression Meets Security: Low-Complexity Linear Collaborative Federated Learning With Enhanced Accuracy

Federated learning (FL) has been regarded as a promising paradigm for enabling distributed model training over resource-limited edge devices. Although FL maintains data locality and enhances model generalization, it faces challenges such as model leakage and pressure from frequent model updates. Some existing schemes, such as differential privacy and model encryption, can partially alleviate these issues while sacrificing the accuracy of the modeling training or increasing the computational overheads in training. To address this issue, we design a low-complexity linear collaborative FL (LCFL) framework to enhance the privacy and accuracy of FL. Specifically, we propose the collaborative secrecy transmission (CST) algorithm by integrating a variant of Shamir’s secret-sharing with the model segmentation, which can compresses and encrypts the local models for FL. The decoding complexity of the CST algorithm is only $O(N^{3})$ under the compression ratio of $N$, which reduces the communication overhead and computational complexity. We conduct a quantitative analysis of the model error induced by the CST algorithm and derive its closed-form upper bound. Within LCFL, we formulate an optimization problem to maximize the global model accuracy in wireless FL by optimizing compression ratios, bandwidth allocation, and transmit-powers. Subsequently, we propose a low-complexity algorithm to solve this problem effectively. Numerical simulations demonstrate the efficacy of LCFL in improving FL’s accuracy and security, and the results validate the efficiency of the proposed optimization scheme for wireless FL.