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

arXiv · AI, language, vision and robotics · article · Aug 27, 2026 · UTC

Edge intelligence systems increasingly require model training and online inference to coexist on resource-constrained devices, while inference demand can vary substantially across tasks over time. This creates two coupled challenges: sufficient computation must be reserved for inference to maintain service-level objectives (SLOs), while the remaining training capacity should adapt to task-specific demand so that frequently requested tasks can improve earlier during training. We propose an SLO-aware, demand-driven multitask federated learning framework (DART-FL) that jointly adapts the inferenc

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