arXiv Machine Learning August 27, 2026 By Nguyen Van Thieu, Ti Ti Nguyen, Ons Aouedi, Vu Nguyen Ha, Symeon Chatzinotas

Resilient Decentralized Wireless Federated Learning via Gradient Tracking with AdamW

Read the original on arXiv Machine Learning →

The paper introduces QEF-GT-AdamW, a communication‑efficient and outage‑resilient algorithm for decentralized wireless federated learning. It combines gradient tracking, AdamW adaptive optimization, and dual‑stream biased quantization with error feedback to reduce communication payloads while mitigating non‑IID data effects. The method includes a local fallback strategy for unreliable broadcasts and provides convergence guarantees under compressed, unreliable wireless communication, demonstrating improved robustness and accuracy‑communication trade‑offs on heterogeneous MNIST and CIFAR‑10 datasets.

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