提出动态系统建模的联邦学习算法，提升异构环境下的模型性能。
FedECADO: A Dynamical System Model of Federated Learning
- 基于动力学系统建模联邦学习过程，捕捉客户端差异。
- 在多种异构场景下，分类准确率优于FedProx和FedNova。
- 适合处理数据分布不均、计算能力差异大的实际部署场景。
联邦学习利用分布式优化在多个客户端间训练统一的机器学习模型。然而，数据分布异构和计算负载不均会导致更新不一致，影响模型性能。本文提出FedECADO，一种受联邦学习过程动力学系统表示启发的新算法。通过聚合敏感度模型反映各客户端处理的数据量，应对非独立同分布（non-IID）数据问题；设计多速率积分方法与自适应步长选择，在连续时间中同步活跃客户端的更新，以缓解异构计算带来的挑战。相比主流方法（如FedProx和FedNova），FedECADO在多种异构场景下实现了更高的分类准确率。
原文摘要 · Abstract (English)
Federated learning harnesses the power of distributed optimization to train a unified machine learning model across separate clients. However, heterogeneous data distributions and computational workloads can lead to inconsistent updates and limit model performance. This work tackles these challenges by proposing FedECADO, a new algorithm inspired by a dynamical system representation of the federated learning process. FedECADO addresses non-IID data distribution through an aggregate sensitivity model that reflects the amount of data processed by each client. To tackle heterogeneous computing, we design a multi-rate integration method with adaptive step-size selections that synchronizes active client updates in continuous time. Compared to prominent techniques, including FedProx and FedNova, FedECADO achieves higher classification accuracies in numerous heterogeneous scenarios.
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