Summary of Asynchronous Multi-server Federated Learning For Geo-distributed Clients, by Yuncong Zuo et al.
Asynchronous Multi-Server Federated Learning for Geo-Distributed Clients
by Yuncong Zuo, Bart Cox, Lydia Y. Chen, Jérémie Decouchant
First submitted to arxiv on: 3 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed federated learning (FL) architecture tackles scalability limitations by introducing a multi-server system that is entirely asynchronous, addressing server idle time and the risk of a single server becoming a bottleneck. The solution keeps servers and clients continuously active, with clients interacting solely with their nearest server and servers updating each other asynchronously. Compared to three representative baselines – FedAvg, FedAsync, and HierFAVG – on MNIST, CIFAR-10, and WikiText-2 datasets, the proposed architecture converges to similar or higher accuracy levels while requiring 61% less time in geo-distributed settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new kind of learning system lets many devices work together without sharing their data. This helps solve a problem where one device becomes too slow or stops working. The new system makes sure all devices are always connected and talking to each other, which improves how well the system works overall. This was tested on three different types of datasets and compared to similar systems. The results showed that this new approach is just as good or even better than existing ones. |
Keywords
» Artificial intelligence » Federated learning
