Powering Multi-Task Federated Learning with Competitive GPU Resource Sharing

Abstract

Federated learning (FL) nowadays involves compound learning tasks as cognitive applications' complexity increases. For example, a self-driving system hosts multiple tasks simultaneously (e.g., detection, classification, 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: (1) As different tasks' skewed data distributions and corresponding models cause highly imbalanced learning workloads, current GPU scheduling methods lack effective resource allocations; (2) 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 address 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: (1) 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. (2) FL could be further improved by taking architectural level coordination into consideration. Our experiments demonstrate that the FL throughput could be significantly escalated.