Federated Learning (FL) enables edge devices or clients to collaboratively
train machine learning (ML) models without sharing their private data. Much of
the existing work in FL focuses on efficiently learning a model for a single
task. In this paper, we study simultaneous training of multiple FL models using
a common set of clients. The few existing simultaneous training methods employ
synchronous aggregation of client updates, which can cause significant delays
because large models and/or slow clients can bottleneck the aggregation. On the
other hand, a naive asynchronous aggregation is adversely affected by stale
client updates. We propose FedAST, a buffered asynchronous federated
simultaneous training algorithm that overcomes bottlenecks from slow models and
adaptively allocates client resources across heterogeneous tasks. We provide
theoretical convergence guarantees for FedAST for smooth non-convex objective
functions. Extensive experiments over multiple real-world datasets demonstrate
that our proposed method outperforms existing simultaneous FL approaches,
achieving up to 46.0% reduction in time to train multiple tasks to completion.

提出了一个缓冲异步联邦同时训练算法 FedAST，通过克服模型缓慢和自适应分配异构任务的客户资源来解决瓶颈问题，实验证明与现有同时联邦学习方法相比，能够达到多任务训练时间的最多 46.0% 的减少。