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arXiv Machine Learning By Yiming Xie, Pinrui Yu, Geng Yuan, Xue Lin, Ningfang Mi

DART-FL: Burst-Aware Multitask Federated Learning under Dynamic Inference Demand at the Edge

Read the original on arXiv Machine Learning →

DART-FL is a multitask federated learning framework designed for edge devices that must balance online inference and model training under limited resources. It dynamically allocates resources between inference and training based on current inference backlog and service capacity, then distributes remaining training capacity among tasks using a queue‑aware scheduler that adjusts loss weights. Experiments on image classification datasets with synthetic and real workloads show that DART‑FL adapts to bursty inference demand, improving accuracy for high‑demand tasks while preserving overall multitask performance.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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