考虑计算与通信负载，优化去中心化联邦学习训练调度。
Load-Aware Training Scheduling for Model Circulation-based Decentralized Federated Learning
- 按节点负载动态调度训练任务，分步求解全局优化问题。
- 在MNIST和CIFAR-10上训练时间减少，收敛速度更快。
- 适合资源不均、数据非独立同分布的分布式训练场景。
本文提出Load-aware Tram-FL，是Tram-FL的扩展，引入训练调度机制以在去中心化联邦学习中最小化总训练时间，同时考虑计算与通信负载。该调度问题被建模为全局优化任务，虽原问题难以求解，但通过分解为节点级子问题得以解决。为在非独立同分布（non-IID）数据下实现均衡数据利用，引入方差约束；目标函数同时最小化整体训练延迟，涵盖计算与通信开销。在MNIST和CIFAR-10上的仿真结果表明，相比基线方法，Load-aware Tram-FL显著减少训练时间并加速收敛。
原文摘要 · Abstract (English)
This paper proposes Load-aware Tram-FL, an extension of Tram-FL that introduces a training scheduling mechanism to minimize total training time in decentralized federated learning by accounting for both computational and communication loads. The scheduling problem is formulated as a global optimization task, which-though intractable in its original form-is made solvable by decomposing it into node-wise subproblems. To promote balanced data utilization under non-IID distributions, a variance constraint is introduced, while the overall training latency, including both computation and communication costs, is minimized through the objective function. Simulation results on MNIST and CIFAR-10 demonstrate that Load-aware Tram-FL significantly reduces training time and accelerates convergence compared to baseline methods.
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