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

Yiming Xie; Pinrui Yu; Geng Yuan; Xue Lin; Ningfang Mi

cs.LGAug 27, 2026

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

Authors: Yiming Xie, Pinrui Yu, Geng Yuan, Xue Lin, Ningfang Mi

Organizations: Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, USA · School of Computing, University of Georgia, Athens, GA, USA

Abstract

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 inference-training resource split and task-level training emphasis. At each scheduling interval, DART-FL uses the inference backlog and profiled service capacity to determine the minimum resource allocation required for inference. The remaining training capacity is then distributed across tasks using a queue-aware DPP-inspired scheduler, and the resulting task allocations are mapped to dynamic loss weights. This allows tasks experiencing higher inference demand to receive greater training emphasis in earlier communication rounds. Clients train a shared backbone with task-specific heads, and the complete multitask model is aggregated through FedAvg. We evaluate DART-FL using Stanford Cars and Oxford Flowers 102 under both synthetic and real Alibaba trace-derived workloads. 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.

Figures & tables

Fig. 1 : Overview of DART-FL for federated multitask learning. Bursty task-specific inference arrivals are incorporated into pre-service queues. The SLO-aware resource allocator first determines the inference resource ratio and assigns the remaining computation capacity to training. The shared queue-aware scheduler then distributes the available training capacity across tasks according to their current inference demand, and the resulting allocation determines the dynamic task weights used by selected clients for local multitask training. Finally, full-model FedAvg aggregates the shared backbone and all task-specific heads.

Panel of Figure 2 — Fig. 2 : Task-specific inference workloads: (a) synthetic and (b) real Alibaba cluster trace, used in our experiments.

Panel of Figure 3 — Fig. 3 : Inference accuracy under the synthetic workload. The dashed lines indicate the burst period in Stanford Cars.

Panel of Figure 4 — Fig. 4 : Inference accuracy under the Alibaba trace-derived workload. The dashed lines indicate the burst periods in Stanford Cars.

Panel of Figure 5 — Fig. 5 : Adaptive scheduling behavior of DART-FL under synthetic and Alibaba trace-derived inference workloads.

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Federated Learning (FL) enables collaborative intelligence across decentralized data source devices in a privacy-preserving way. While substantial research attention has been drawn to optimizing the learning process for an individual task, real-world applications increasingly require multiple machine learning tasks simultaneously training their models across a shared pool of devices. Naively applying single-FL optimization techniques in multi-FL systems results in suboptimal system performance, particularly due to device heterogeneity and resource inefficiency. To address such a critical open challenge, we introduce {\em FedACT}, a novel resource heterogeneity-aware device scheduling approach designed to efficiently schedule heterogeneous devices across multiple concurrent FL jobs, with the goal of minimizing their average job completion time (JCT). {\em FedACT} dynamically assigns devices to FL jobs based on an alignment scoring mechanism that evaluates the compatibility between available resources of devices and resource demands of jobs. Additionally, it incorporates participation fairness to ensure balanced contributions from devices across jobs, further enhancing the accuracy levels of learned global models. An optimal scheduling plan is formulated in {\em FedACT} by prioritizing devices with higher alignment scores, while ensuring fair participation across jobs. To evaluate the effectiveness of the proposed scheduling algorithm, we carried out comprehensive experiments using diverse FL jobs and benchmark datasets. Experimental results demonstrate that {\em FedACT} reduces the average JCT by up to 8.3

\times

and improves model accuracy by up to 44.5%, compared to the state-of-the-art baselines.

Abstract

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