Asynchronous Multi-Server Federated Learning for Geo-Distributed Clients
arxiv(2024)
摘要
Federated learning (FL) systems enable multiple clients to train a machine
learning model iteratively through synchronously exchanging the intermediate
model weights with a single server. The scalability of such FL systems can be
limited by two factors: server idle time due to synchronous communication and
the risk of a single server becoming the bottleneck. In this paper, we propose
a new FL architecture, to our knowledge, the first multi-server FL system that
is entirely asynchronous, and therefore addresses these two limitations
simultaneously. Our solution keeps both servers and clients continuously
active. As in previous multi-server methods, clients interact solely with their
nearest server, ensuring efficient update integration into the model.
Differently, however, servers also periodically update each other
asynchronously, and never postpone interactions with clients. We compare our
solution to three representative baselines - FedAvg, FedAsync and HierFAVG - on
the MNIST and CIFAR-10 image classification datasets and on the WikiText-2
language modeling dataset. Our solution converges to similar or higher accuracy
levels than previous baselines and requires 61
geo-distributed settings.
更多查看译文
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要