AI 中文总结
针对异构衰落信道下空中联邦学习的挑战，提出FedOAG算法，通过梯度归一化和隐式八卦机制实现无偏收敛，达到最优速率，并在真实数据集上验证。
AI 中文摘要
空中计算通过利用波形叠加进行同步模型聚合，已成为在无线网络中部署联邦学习算法的可扩展且高效的解决方案。大多数现有工作难以应对异构衰落信道。这些方法要么强制所有设备进行无偏更新，要么允许部分设备参与贡献，这需要在特定衰落模型下仔细调整收敛界以减轻偏差。然而，前者由于最弱信道而显著放大接收机噪声，而后者对衰落模型失配敏感，且仅收敛到有偏目标。为应对这些挑战，我们提出FedOAG，它采用算法组件通过梯度归一化自动满足能量约束，并通过隐式八卦机制均匀混合设备更新。重要的是，FedOAG不要求所有设备传输，也不依赖特定衰落模型或时变统计信道分布的知识。我们证明FedOAG以任何随机一阶方法的最佳可能速率$O(1/\sqrt{T})$收敛到无偏非凸目标的驻点。我们通过在真实世界数据集上的动态无线条件下的数值实验证实了我们的分析。
英文摘要
Over-the-air computation has emerged as a scalable and efficient solution for deploying federated learning algorithms in wireless networks by exploiting waveform superposition for simultaneous model aggregation. Most existing work struggles with heterogeneous fading channels. These approaches either enforce unbiased updates from all devices or allow partial device contributions, requiring careful tuning of the convergence bound to mitigate bias under specific fading models. However, the former significantly amplifies receiver noise due to the weakest channel, whereas the latter is sensitive to fading model mismatch and converges only to a biased objective. To tackle these challenges, we propose FedOAG, which employs algorithmic components to automatically satisfy energy constraints via gradient normalization and evenly mix devices' updates through implicit gossiping. Importantly, FedOAG does not require transmission from all devices, nor does it rely on a specific fading model or knowledge of time-varying statistical channel distributions. We show that FedOAG converges to a stationary point of an unbiased non-convex objective at the best possible rate $O(1/\sqrt{T})$ for any stochastic first-order method. We corroborate our analysis with numerical experiments over dynamic wireless conditions on real-world datasets.
CommentsMobiHoc 2026 (the 27th International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing)
