Federated Learning with CSMA Based User Selection for IoT Applications

User selection has became crucial for improving energy efficiency in communication of federated learning (FL) over wireless networks. However, centralized user selection causes additional system complexity. This study proposes a network intrinsic approach of distributed user selection that leverages the radio resource competition mechanism in random access. Taking the carrier sensing multiple access (CSMA) mechanism as an example of random access, we manipulate the contention window (CW) size to prioritize certain users for obtaining radio resources in each round of training. Training data bias is used as a target scenario for FL with user selection. Prioritization is based on the distance between the newly trained local model and the global model of the previous round. To avoid “excessive contribution” by certain users, a counting mechanism is used to ensure fairness. Simulations with various datasets demonstrate that the proposed method can rapidly achieve convergence similar to that of the centralized user selection approach.