FedACT: Concurrent Federated Intelligence across Heterogeneous Data Sources

FedACT：跨异构数据源的并发联邦智能

Federated Learning (FL) enables collaborative intelligence across decentralized data source devices in a privacy-preserving way. While substantial research attention has been drawn to optimizing the learning process for an individual task, real-world applications increasingly require multiple machine learning tasks simultaneously training their models across a shared pool of devices. 联邦学习（FL）能够在保护隐私的前提下，实现跨去中心化数据源设备的协作智能。尽管目前已有大量研究关注于优化单个任务的学习过程，但现实世界的应用日益要求多个机器学习任务在共享的设备池中同时进行模型训练。

Naively applying single-FL optimization techniques in multi-FL systems results in suboptimal system performance, particularly due to device heterogeneity and resource inefficiency. To address such a critical open challenge, we introduce {\em FedACT}, a novel resource heterogeneity-aware device scheduling approach designed to efficiently schedule heterogeneous devices across multiple concurrent FL jobs, with the goal of minimizing their average job completion time (JCT). 在多联邦学习系统中简单地应用单联邦学习优化技术，往往会导致系统性能不佳，这主要是由于设备异构性和资源效率低下所致。为了解决这一关键的开放性挑战，我们引入了 {\em FedACT}，这是一种新型的资源异构感知设备调度方法，旨在跨多个并发联邦学习任务高效调度异构设备，目标是最小化它们的平均任务完成时间（JCT）。

{\em FedACT} dynamically assigns devices to FL jobs based on an alignment scoring mechanism that evaluates the compatibility between available resources of devices and resource demands of jobs. Additionally, it incorporates participation fairness to ensure balanced contributions from devices across jobs, further enhancing the accuracy levels of learned global models. {\em FedACT} 基于一种对齐评分机制动态地将设备分配给联邦学习任务，该机制评估设备可用资源与任务资源需求之间的兼容性。此外，它还纳入了参与公平性，以确保设备在不同任务间贡献的平衡，从而进一步提高所学习全局模型的准确度水平。

An optimal scheduling plan is formulated in {\em FedACT} by prioritizing devices with higher alignment scores, while ensuring fair participation across jobs. To evaluate the effectiveness of the proposed scheduling algorithm, we carried out comprehensive experiments using diverse FL jobs and benchmark datasets. Experimental results demonstrate that {\em FedACT} reduces the average JCT by up to 8.3(\times) and improves model accuracy by up to 44.5%, compared to the state-of-the-art baselines. {\em FedACT} 通过优先考虑具有更高对齐评分的设备，同时确保跨任务的公平参与，从而制定出最优的调度计划。为了评估所提调度算法的有效性，我们使用多种联邦学习任务和基准数据集进行了全面的实验。实验结果表明，与当前最先进的基准方法相比，{\em FedACT} 将平均任务完成时间（JCT）缩短了高达 8.3 倍，并将模型准确度提高了高达 44.5%。