Client Scheduling for Unreliable Semi-Decentralized Federated Learning

The Industrial Internet of Things (IIoT) is emerging as a promising technology that can accelerate the application of industrial intelligence. Because of the sensitive nature of user data, federated learning (FL) which performs distributed machine learning while preserving data privacy, is developed to meet the accuracy and privacy requirements of IIoT end devices/clients. However, the unreliable communications in IIoT may negatively affect the training efficiency. In this paper, we study on the client scheduling problem in a multi-server FL framework for the communication reliability and training efficiency improvement. A client-server association optimization problem is formulated, with the objective of minimizing the global training loss. Resorting to the convergence analysis of SD-FL, the original problem is simplified and transformed to guide us to design a high-efficiency client scheduling scheme. Finally, simulation results show that the proposed scheme significantly outperforms the baselines in terms of the test accuracy and training loss.