Summary of Federated Frank-wolfe Algorithm, by Ali Dadras et al.
Federated Frank-Wolfe Algorithm
by Ali Dadras, Sourasekhar Banerjee, Karthik Prakhya, Alp Yurtsever
First submitted to arxiv on: 19 Aug 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 The paper proposes Federated Frank-Wolfe Algorithm (FedFW), an innovative approach to federated learning that addresses the challenges of constrained machine learning problems, particularly when the projection step is costly. FedFW achieves data privacy, low per-iteration cost, and communication efficiency for sparse signals. The algorithm’s performance is evaluated in both deterministic and stochastic settings, demonstrating its ability to find -suboptimal solutions within O(^{-2}) iterations for smooth and convex objectives, and O(^{-3}) iterations for smooth but non-convex objectives. The authors also demonstrate the empirical performance of FedFW on several machine learning tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to collaborate on learning systems while keeping data private. It’s called Federated Frank-Wolfe Algorithm (FedFW). This method is better at handling difficult problems when the process of adjusting and refining the results is time-consuming. The algorithm does this while maintaining privacy, being efficient in its calculations, and only sending a small amount of information. The authors tested it on several learning tasks and showed that it can get good results quickly. |
Keywords
» Artificial intelligence » Federated learning » Machine learning
