@unpublished{yu2022powering,
author = {Yu, Yongbo and Yu, Fuxun and Xu, Zirui and Wang, Di and Zhang, Minjia and Li, Ang and Bray, Shawn and Liu, Chenchen and Chen, Xiang},
title = {Powering Multi-Task Federated Learning with Competitive GPU Resource Sharing},
year = {2022},
month = {April},
abstract = {Federated learning has been applied to train different tasks, posing new computation challenges in training, especially when the scenario becomes multi-task. In this paper, we profile the FL multi-task training process at the operator-level to identify and solve the problems in FL multi-task training. Second, we propose a Competitive GPU Resource Sharing method that can efficiently partition GPU resources to improve training efficiency. Third, for the imbalanced data problem in FL with multi-device training, we perform GPU resource partitioning according to the workload of different models. Experiments show that our method can obtain a 2.1 times speedup.},
url = {http://approjects.co.za/?big=en-us/research/publication/powering-multi-task-federated-learning-with-competitive-gpu-resource-sharing/},
}