Summary of On the Convergence Of Federated Learning Algorithms Without Data Similarity, by Ali Beikmohammadi et al.
On the Convergence of Federated Learning Algorithms without Data Similarity
by Ali Beikmohammadi, Sarit Khirirat, Sindri Magnússon
First submitted to arxiv on: 29 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Science and Game Theory (cs.GT)
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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 The paper presents a unified framework for analyzing the convergence of federated learning algorithms without relying on data similarity assumptions. The authors develop an inequality that captures the influence of step sizes on algorithmic convergence performance, which is independent of data similarity conditions. They apply their theorems to well-known federated algorithms and derive precise expressions for three widely used step size schedules: fixed, diminishing, and step-decay step sizes. The paper also conducts comprehensive evaluations of the performance of these federated learning algorithms on benchmark datasets under varying data similarity conditions, demonstrating significant improvements in convergence speed and overall performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning is a way for machines to work together without sharing all their data. Usually, people adjust how much each machine learns based on how similar their data is. But this can be slow if the data isn’t very similar. The researchers created a new way to understand how these algorithms converge (or improve) without needing to know how similar the data is. They used math to figure out three different ways to choose how much each machine learns, and tested them on big datasets with varying levels of similarity. They found that their methods worked better and faster than before. |
Keywords
* Artificial intelligence * Federated learning
