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

arXiv · AI, language, vision and robotics · article · Sep 14, 2026 · UTC

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

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