Decentralized Federated Learning (DFL) over lossy wireless networks faces two key challenges: selection bias, where updates from poor-quality links are systematically underrepresented due to partial model reception, and update staleness, where asynchronous nodes contribute outdated information. We show that uniform gossip aggregation with local-fill reconstruction introduces persistent link-quality-induced bias, while completeness-based weighting further amplifies this effect. To address these challenges, we propose DFL-AA (Decentralized Federated Learning with Adaptive AoI-weighted Aggregation), which combines Inverse Probability Weighting with online EWMA-based channel estimation to correct selection bias and Age-of-Information-based weighting to mitigate staleness without requiring global synchronization. We theoretically show that DFL-AA removes link-quality distortion in expectation and experimentally demonstrate consistent improvements over state-of-the-art baselines across varying loss rates, network sizes, and heterogeneous wireless conditions.

翻译：去中心化联邦学习（DFL）在不可靠无线网络中面临两大关键挑战：一是选择偏差，即因模型部分接收导致来自劣质链路的数据更新被系统性低估；二是更新过时问题，即异步节点贡献过时信息。研究表明，采用局部填充重建的均匀八卦聚合会诱发持续性链路质量偏差，而基于完整性的加权策略进一步放大了该效应。针对上述挑战，我们提出DFL-AA（自适应信息年龄加权聚合的去中心化联邦学习），该方法结合逆概率加权与基于在线EWMA的信道估计来校正选择偏差，并通过信息年龄加权策略在无需全局同步条件下缓解更新过时问题。理论证明DFL-AA能在期望意义上消除链路质量失真，实验结果显示该方法在不同丢包率、网络规模及异构无线环境下均持续优于现有最优基线方法。