Air Federated Learning

Air Federated Learning (AirFL) aims to improve the efficiency of federated learning by leveraging the superposition property of wireless channels to aggregate model updates from multiple devices simultaneously, reducing communication overhead and latency. Current research focuses on optimizing various aspects of AirFL, including client selection strategies, weighted aggregation techniques, and the use of advanced coding and modulation schemes to mitigate the effects of channel noise and heterogeneity. This approach holds significant promise for enabling large-scale distributed machine learning in resource-constrained environments, particularly in applications like the Internet of Things and edge computing, by improving both privacy and efficiency.