Decentralized Federated Learning(DFL) enables collaborative model training across wireless edge nodes, including IoT deployments, autonomous vehicles, UAV swarms, and satellite constellations. Operating over lossy wireless links under constraints, these systems cannot rely on retransmissions, so model parameters must be accepted as partial chunks, leading to two key failure modes, which are selection bias, where poor-quality links are systematically under-represented in gossip aggregation, and update staleness, where asynchronous nodes contribute outdated models. We prove that classical gossip aggregation introduces irreducible selection bias proportional to the link-loss rate. We propose DFL-AA (Decentralized Federated Learning with Adaptive AoI-weighted Aggregation), which corrects selection bias using Inverse Probability Weighting (IPW) with online channel estimation and mitigates staleness via Age-of-Information (AoI) decay without requiring a global clock. We prove that DFL-AA removes link-quality distortion in expectation and consistently outperforms state-of-the-art baselines across varying loss rates and heterogeneous channel conditions on fixed directed topologies.

翻译：去中心化联邦学习(DFL)能够在无线边缘节点（包括物联网部署、自动驾驶车辆、无人机集群及卫星星座）间实现协同模型训练。由于在约束条件下运行于有损无线链路，此类系统无法依赖重传机制，因而模型参数必须以分块形式接收，由此引发两类关键故障模式：选择性偏差——低质量链路在高斯混合聚合中被系统性低估；以及更新过时——异步节点贡献过期模型。我们证明经典高斯混合聚合会引入与链路损失率成正比且不可消除的选择性偏差。为此提出DFL-AA（基于自适应AoI加权聚合的去中心化联邦学习），该方法通过在线信道估计与逆概率加权(IPW)校正选择性偏差，并利用信息时效(AoI)衰减机制缓解更新过时问题，无需全局时钟。我们证明DFL-AA能在期望意义上消除链路质量失真，并在固定有向拓扑下不同损失率及异构信道条件中，持续优于现有最优基线方法。