cs.LGAug 26, 2026

Resilient Decentralized Wireless Federated Learning via Gradient Tracking with AdamW

Authors: Nguyen Van Thieu, Ti Ti Nguyen, Ons Aouedi, Vu Nguyen Ha, Symeon Chatzinotas

Organizations: Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, Luxembourg

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Abstract

Wireless Internet-of-Things (IoT) edge networks require decentralized learning (DecL) methods that can operate reliably under both heterogeneous local data and communication-constrained wireless links. However, existing decentralized optimization schemes often incur substantial communication overhead and degraded performance when transmissions are constrained by strict airtime budgets, fading channels, and packet losses. This paper proposes QEF-GT-AdamW, a communication-efficient and outage-resilient algorithm for DecL over wireless communication (WCom) networks. The proposed method combines gradient tracking to mitigate the effect of non-IID data, AdamW-based adaptive optimization to improve training stability, and dual-stream biased quantization with error feedback to reduce communication payloads for both model and tracking exchanges. To address unreliable broadcast communication, the proposed framework further employs a local fallback strategy when scheduled packets are not successfully received. We explicitly model the effect of bandwidth, transmit power, airtime constraints, and fading channels on DecL performance, and establish convergence guarantees for the proposed algorithm under compressed and unreliable wireless communication. Experimental results on heterogeneous MNIST and CIFAR-10 settings show that QEF-GT-AdamW consistently improves robustness and convergence performance over representative DecL baselines while achieving favorable accuracy-communication trade-offs under limited wireless resources.

Figures & tables

Fig. 1 : A TS-based learning and information exchange framework.
Fig. 2 : Non-IID partition of MNIST and CIFAR10 dataset
Fig. 3 : Effect of Top- KK density on test accuracy and training loss, with convergence curves for MNIST (first row) and CIFAR-10 (second row).
Fig. 4 : Final average accuracy and average packet drop rate versus bandwidth (MHz) under wireless deadlines and outage-based drops
Fig. 5 : Final average accuracy and average packet drop rate versus transmission power (Watt) under wireless deadlines and outage-based drops.
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Decentralized learning trains models through local computation and peer-to-peer information exchange rather than relying on a central coordinator.

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