Summary of Federated Learning Under Periodic Client Participation and Heterogeneous Data: a New Communication-efficient Algorithm and Analysis, by Michael Crawshaw et al.
Federated Learning under Periodic Client Participation and Heterogeneous Data: A New Communication-Efficient Algorithm and Analysis
by Michael Crawshaw, Mingrui Liu
First submitted to arxiv on: 30 Oct 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 Amplified SCAFFOLD algorithm achieves linear speedup, reduced communication, and resilience to data heterogeneity in non-convex optimization. It handles participation patterns where each client has an equal chance of participating over a fixed window of rounds, including cyclic client availability as a special case. The algorithm is shown to be effective in reducing communication rounds compared to prior work, with improved dependency on epsilon and kappa. Experimental results with synthetic and real-world data demonstrate the effectiveness of Amplified SCAFFOLD under periodic client participation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces an algorithm called Amplified SCAFFOLD that helps devices learn together without always needing to be connected. This is important because devices often don’t have a constant internet connection. The algorithm makes sure that all devices get a chance to participate in the learning process, and it does this efficiently by reducing the amount of data that needs to be shared between devices. The paper shows that this algorithm works well with both fake and real-world data. |
Keywords
* Artificial intelligence * Optimization
