Asynchronous federated learning for tactical autonomy
Earl Tankard, Desta Haileselassie Hagos, Danda B. Rawat · 2023
Federated learning has been an active area of research for secure and privacy-aware learning where parameters and models are shared instead of actual data. However, there are some challenges to be addressed since federated learning relies on the aggregation process for the global model. If the learning server waits to receive all updates from all clients too long for the optimal global model, there might be a high delay. In this paper, we will design, develop and evaluate asynchronous federated learning by considering asynchronous client training and asynchronous aggregation that is applicable to tactical scenarios.