Summary of Stabilized Proximal-point Methods For Federated Optimization, by Xiaowen Jiang et al.
Stabilized Proximal-Point Methods for Federated Optimization
by Xiaowen Jiang, Anton Rodomanov, Sebastian U. Stich
First submitted to arxiv on: 9 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC)
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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 This paper presents a novel distributed algorithm called S-DANE for Federated Learning, which addresses the challenge of communication constraints in optimization. Building on DANE, a widely known distributed proximal-point algorithm, S-DANE uses an auxiliary sequence of prox-centers to maintain deterministic communication complexity while improving local computation efficiency. The algorithm is designed to support partial client participation and arbitrary stochastic local solvers, making it practical for real-world applications. Experimental results show that S-DANE achieves the best-known communication complexity among all existing methods for distributed convex optimization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary S-DANE is a new way to do Federated Learning efficiently. It’s like a team working together on a problem. The team members (called clients) each work on their own part of the problem, and then they share their answers with the team leader (the server). S-DANE helps the clients do their work better by giving them hints about how to improve their answers. This makes it faster and more accurate than other methods. |
Keywords
* Artificial intelligence * Federated learning * Optimization
