Geng Yuan

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

Results show that DART-FL dynamically adapts the inference-training resource split to time-varying inference demand and shifts the learning progress of high-demand tasks toward their burst periods, improving model accuracy when those tasks are frequently requested while maintaining comparable long-term multitask performance.