Federated Learning over a Wireless Network: Distributed User Selection through Random Access
This addresses communication efficiency for federated learning in wireless settings, but it is incremental as it builds on existing user selection methods.
The study tackled the problem of high communication costs in federated learning over wireless networks by proposing a distributed user selection method using random access, which achieved convergence similar to centralized approaches in simulations.
User selection has become crucial for decreasing the communication costs 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 this method can rapidly achieve convergence similar to that of the centralized user selection approach.