Hierarchically Fair and Differentially Private Federated Learning in Industrial IoT Based on Compressed Sensing With Adaptive-Thresholding Sparsification

Federated learning (FL) enables decentralized industrial-Internet-of-Things devices (also called clients) to share model parameters to build a joint model. Fair rewards, security of shared data, and transmission cost are the important factors that influence clients to participate in FL. Few existing works can solve these problems at the same time. Therefore, we propose a hierarchically fair and differentially private federated learning (HFDPFL), which regards the model itself as a reward to promote fairness. Reputation is used to measure the client's contribution to FL, and clients with high reputation will be rewarded with high accuracy models. In order to ensure the security of the shared data and reduce communication overhead, we implement differentially private gradient compression based on compressed sensing, which achieves differential privacy protection of gradients and improves communication efficiency. Extensive experiments are conducted to demonstrate the superiority of HFDPFL in terms of fairness, privacy preserving, and communication efficiency.