发表机构
Southern University of Science and Technology; Pengcheng Laboratory（南方科技大学; 鹏城实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对异构边缘网络中同步ADMM因等待慢客户端而效率低的问题，提出P-GADMM，按计算能力分组并结合有界异步协调，在保证收敛的同时减少训练时间并保持精度。
AI 中文摘要
交替方向乘子法（ADMM）广泛用于分布式优化，但其同步实现在异构边缘网络中可能遭受效率损失，因为快速客户端或组在全局更新完成前需要等待较慢的客户端。现有的基于组的ADMM方法通过分组减少通信开销，但其分组规则通常关注数据相似性或网络拓扑，并未明确考虑计算异构性。为解决此问题，本文提出并行分组ADMM（P-GADMM）用于异构边缘网络中的分布式优化。P-GADMM根据客户端计算能力和本地数据大小形成计算感知的边缘组，减少了组内训练速度的差异。它进一步将边缘级聚合与云端有界异步协调相结合，允许活跃组参与全局更新而无需等待较慢的组，同时通过延迟阈值控制过时的组信息。对于强凸的组目标，我们在有界组级陈旧性下建立了P-GADMM理想化形式的收敛保证，显示出时间平均的收敛行为，直至陈旧性引起的渐近误差邻域。实验表明，与代表性基线相比，P-GADMM减少了挂钟训练时间，同时保持了相当的最终精度。
英文摘要
The Alternating Direction Method of Multipliers (ADMM) is widely used for distributed optimization, but its synchronous implementation can suffer from efficiency loss in heterogeneous edge networks, where fast clients or groups need to wait for slower ones before global updates can be completed. Existing group-based ADMM methods reduce communication overhead through grouping, but their grouping rules usually focus on data similarity or network topology and do not explicitly account for computation heterogeneity. To address this issue, this paper proposes Parallel Group-Based ADMM (P-GADMM) for distributed optimization in heterogeneous edge networks. P-GADMM forms computation-aware edge groups according to client computational capabilities and local data sizes, which reduces training-speed variation within each group. It further combines edge-level aggregation with bounded asynchronous coordination at the cloud, allowing active groups to participate in global updates without waiting for slower groups while controlling stale group information through a delay threshold. For strongly convex group objectives, we establish convergence guarantees for an idealized form of P-GADMM under bounded group-level staleness, showing a time-averaged convergence behavior up to a staleness-induced asymptotic error neighborhood. Experiments show that P-GADMM reduces wall-clock training time compared with representative baselines while maintaining comparable final accuracy.
Comments13 pages, 12 figures. Submitted to IEEE Transactions on Mobile Computing (TMC)
