Channel-Adaptive Robust Aggregation for Over-the-Air Federated Learning in Heterogeneous Networks
For federated learning in wireless networks, this work addresses the joint challenges of noise, fading, and client heterogeneity, but the improvements are incremental over existing OTA-FL methods.
The paper proposes CHARGE-FL, a framework for over-the-air federated learning that adaptively schedules aggregation based on channel dynamics and application readiness, achieving superior accuracy, stability, and convergence compared to existing methods in heterogeneous wireless networks.
The growing demand for privacy-preserving, data-intensive applications such as IoT, augmented reality, and autonomous systems positions Federated Learning (FL) as a key enabler in 6G networks. Over-the-Air FL (OTA-FL) leverages the superposition property of the wireless multiple access channel for efficient aggregation via simultaneous transmissions. Existing methods rely on fixed aggregation schedules and do not jointly address noise, fading, and client heterogeneity. We propose CHARGE-FL (CHannel-Adaptive Robust agGrEgation), a framework that adaptively schedules aggregation based on channel dynamics and application readiness. By combining a tailored optimization strategy with a dual-purpose precoding mechanism, CHARGE-FL mitigates channel distortion and bias from partial updates, achieving superior accuracy, stability, and convergence under realistic wireless conditions. Empirical results under realistic wireless conditions show that CHARGE-FL significantly improves accuracy, stability, and convergence over state-of-the-art OTA-FL methods, particularly in straggler-prone and noisy scenarios.