FedMT: Multitask Federated Learning With Competitive GPU Resource Sharing

Federated learning (FL) nowadays involves heterogeneous compound learning tasks as cognitive applications’ complexity increases. For example, a self-driving system hosts multiple tasks simultaneously (e.g., detection, classification, segmentation, etc.) and expects FL to retain life-long intelligence involvement. However, our analysis demonstrates that, when deploying compound FL models for multiple training tasks on a GPU, certain issues arise: As different tasks’ skewed data distributions and corresponding models cause highly imbalanced learning workloads, current GPU scheduling methods lack effective resource allocations; Therefore, existing FL schemes, only focusing on heterogeneous data distribution but runtime computing, cannot practically achieve optimally synchronized federation. To address these issues, we propose a full-stack FL optimization scheme to tackle both intra-device GPU scheduling and inter-device FL coordination for multi-task training. Specifically, our works illustrate two key insights in this research domain: Competitive resource sharing is beneficial for parallel model executions, and the proposed concept of “virtual resource” could effectively characterize and guide the practical per-task resource utilization and allocation; Additionally, architectural-level coordination improves FL performance by aligning task workloads with GPU utilization. Our experiments demonstrate that the FL performance could be significantly escalated. Specifically, we observed a 2.16×–2.38× increase in intra-device GPU training throughput and a 2.53×–2.80× boost in inter-device FL coordination efficiency across diverse multi-task scenarios.