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Channel-Adaptive Robust Aggregation for Over-the-Air Federated Learning in Heterogeneous Networks

arXiv 2607.04218 ↗First posted 5 Jul 2026cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 13 of its 18 references that are in the map. First posted on arXiv 5 Jul 2026.

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13 of 18 references.

References · 13

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  3. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,229
  4. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 906
  5. 2018Federated Learning via Over-the-Air Computationcited by 219
  6. 2020Over-the-Air Federated Learning from Heterogeneous Datacited by 73
  7. 2019On Analog Gradient Descent Learning over Multiple Access Fading Channelscited by 62
  8. 20216G Internet of Things: A Comprehensive Surveycited by 39
  9. 2020Straggler-aware Distributed Learning: Communication Computation Latency Trade-offcited by 15
  10. 2022Over-the-Air Federated Learning with Joint Adaptive Computation and Power Controlcited by 9
  11. 2022CHARLES: Channel-Quality-Adaptive Over-the-Air Federated Learning over Wireless Networkscited by 6
  12. 2024Adaptive Federated Learning Over the Aircited by 4
1 more
  1. 2025Noise Resilient Over-The-Air Federated Learning In Heterogeneous Wireless Networkscited by 1

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  1. 2026Over-the-Air Federated Learning in Heterogeneous Mobile Wireless Networks
  2. 2025Non-Convex Over-the-Air Heterogeneous Federated Learning: A Bias-Variance Trade-off
  3. 2023Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future Directions
  4. 2025Biased Federated Learning under Wireless Heterogeneity
  5. 2022Over-the-Air Federated Learning via Second-Order Optimization
  6. 2023Channel and Gradient-Importance Aware Device Scheduling for Over-the-Air Federated Learning
  7. 2024Mixed-Precision Federated Learning via Multi-Precision Over-The-Air Aggregation
  8. 2025Over-the-Air Federated Learning: Rethinking Edge AI Through Signal Processing