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Despite the recent advancements achieved by federated learning (FL), its real-world deployment is significantly impeded by the heterogeneous learning environment, specifically manifesting as devices with various computing capabilities, non-I.I.D. (Independent Identically and Distributed) data distribution and dynamic wireless transmission conditions. Such learning heterogeneity greatly harms the learning performance, e.g., convergence and learning accuracy. Therefore, we introduce the AMA-FES (adaptive-mixing aggregation, feature-extractor sharing) framework with an asynchronous aggregation scheme to address these challenges. To mitigate the impact of the non-I.I.D. data, we propose the AMA scheme to maintain the training stability by compromising between the previous global model and the synchronised local model updates, avoiding abrupt changes to a completely new model. To reduce computation load, we introduce the FES scheme, enabling the computing-limited devices to update only the classifier. To address the asynchronous model updates caused by the transmission delay, we perform asynchronous aggregation with staleness-based weighting. We implement the AMA-FES framework in a practical scenario where mobile UAVs act as FL training clients to conduct image classification tasks. The experimental results validate the effectiveness of the AMA-FES scheme in restoring training stability and learning accuracy without causing extra computation or communication expenditures in heterogeneous wireless networks.

Federated Learning in Heterogeneous Wireless Networks With Adaptive Mixing Aggregation and Computation Reduction