Sylvas: Synergistic Learning Value based Device Scheduling in Federated Continual Learning

2026-09-14

AI Summary

本文提出Sylvas框架，通过评估设备数据的分布价值和标签价值来解决联邦持续学习中资源受限下的设备调度问题。

Abstract

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.

Problem

• Federated Continual Learning
• Device Scheduling
• Learning Value
• Resource Constraints
• Spatio-temporal Distribution

Innovation

• Synergistic Learning Value
• Device Scheduling
• Federated Continual Learning
• Distributional Value
• Label Value