Summary of Asynchronous Federated Stochastic Optimization For Heterogeneous Objectives Under Arbitrary Delays, by Charikleia Iakovidou et al.
Asynchronous Federated Stochastic Optimization for Heterogeneous Objectives Under Arbitrary Delays
by Charikleia Iakovidou, Kibaek Kim
First submitted to arxiv on: 16 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary As federated learning aims to securely train models with decentralized data, two major challenges arise: slow training times due to straggling clients and accuracy decline under non-iid data distributions (“client drift”). To address these issues, we propose Asynchronous Exact Averaging (AREA), a stochastic gradient algorithm that leverages asynchronous communication for faster convergence and scalability. AREA also employs client memory to correct “client drift” caused by varying update frequencies. This method is guaranteed to converge under arbitrary delays without delay-adaptive stepsizes. For strongly convex functions, AREA asymptotically converges to an error neighborhood dependent on the stochastic gradient variance. For convex non-smooth functions, AREA matches the centralized stochastic subgradient method’s convergence rate up to a constant factor dependent on average client update frequencies. Our results validate our analysis and show AREA outperforms state-of-the-art methods when local data are highly non-iid, especially with many clients. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning helps computers learn from lots of different places without sharing their data. But this makes it slow and less accurate. To fix this, we created a new way to train models called Asynchronous Exact Averaging (AREA). It’s faster because it can send updates at different times, and it’s better because it corrects when the updates are different. This method is special because it works even if some computers take longer than others to update. We tested it and found that it does a great job of training models with lots of different data. |
Keywords
» Artificial intelligence » Federated learning
