Summary of Analysis Of Regularized Federated Learning, by Langming Liu and Dingxuan Zhou
Analysis of regularized federated learning
by Langming Liu, Dingxuan Zhou
First submitted to arxiv on: 3 Nov 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 Federated learning is a machine learning approach that enables processing heterogeneous big data while preserving privacy. The method uses regularization to control communication between central and local machines. Stochastic gradient descent is commonly employed to reduce communication costs on diverse data sets. This paper presents Loopless Local Gradient Descent, an algorithm that optimizes expected communications by controlling probability levels. By allowing flexible step sizes, the method improves performance. Novel analysis is conducted for convergence in both strongly convex and non-convex settings. In the non-convex case, Polyak-Łojasiewicz condition-satisfied smooth objective functions are analyzed, while in the strongly convex setting, a sufficient and necessary condition for expected convergence is provided. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning helps us process big data without sharing it. This new way of learning allows machines to work together while keeping their information private. The algorithm used here is called Loopless Local Gradient Descent. It makes sure that machines don’t need to share too much information by controlling the probability of sharing. This helps reduce communication costs and improves performance. The researchers studied how this method works in different situations, including ones where the data isn’t perfectly linear or curved. They found out what makes it work well in these cases. |
Keywords
» Artificial intelligence » Federated learning » Gradient descent » Machine learning » Probability » Regularization » Stochastic gradient descent
