Sylvas：联邦持续学习中基于协同学习价值的设备调度
Sylvas: Synergistic Learning Value based Device Scheduling in Federated Continual Learning
查看机构详情
- Beijing Jiaotong University（北京交通大学）
- Tsinghua University（清华大学）
- Beijing National Research Center for Information Science and Technology（北京信息科学与技术国家研究中心）
机构由 AI 辅助整理，请以论文原文为准。
浏览论文内容
中文总结 AI 辅助
本文提出Sylvas框架，通过协同学习价值指标评估设备数据分布与标签价值，在资源约束下调度高价值设备，实现联邦持续学习中的及时模型更新与未标记数据利用。
中文摘要 AI 辅助
联邦持续学习（FCL）使共享的全局模型能够持续适应分布式和非平稳的数据流，这使得它在物联网应用中具有重要意义，例如智能交通、工业监控和无人系统。在时空数据分布动态和标签稀缺的情况下，一个关键挑战是如何量化每个边缘设备对全局学习性能的贡献，并在资源约束下调度最有价值的设备以进行及时的模型更新。本文提出了Sylvas，一种用于无线边缘FCL的基于协同学习价值的设备调度框架。Sylvas从两个角度评估分布式数据的学习价值：分布价值，它从时空分布的角度表征设备数据对全局模型学习的贡献；以及标签价值，它捕捉伪标签数据的数量与可靠性之间的权衡。通过将这些因素整合到一个协同学习价值指标中，Sylvas在满足通信和计算资源约束的同时，调度具有高学习价值的设备。案例研究表明，Sylvas支持在时空分布动态下的及时模型适应，并有效利用未标记数据。
英文摘要
Federated continual learning (FCL) enables shared global models to continuously adapt to distributed and non-stationary data streams, making it important for Internet of Things applications such as intelligent transportation, industrial monitoring, and unmanned systems. Under spatio-temporal data distribution dynamics and label scarcity, a key challenge is how to quantify the contribution of each edge device to global learning performance and schedule the most valuable devices under resource constraints for timely model updating. This article presents Sylvas, a synergistic learning value based device scheduling framework for FCL at the wireless edge. Sylvas evaluates the learning value of distributed data from two perspectives: distributional value, which characterizes the contribution of device data to global model learning from a spatio-temporal distribution perspective, and label value, which captures the quantity and reliability tradeoff of pseudo-labeled data. By integrating these factors into a synergistic learning value metric, Sylvas schedules devices with high learning value while satisfying communication and computation resource constraints. Case studies demonstrate that Sylvas supports timely model adaptation under spatio-temporal distribution dynamics and effectively exploits unlabeled data.
