Summary of Fedmodule: a Modular Federated Learning Framework, by Chuyi Chen et al.
FedModule: A Modular Federated Learning Framework
by Chuyi Chen, Zhe Zhang, Yanchao Zhao
First submitted to arxiv on: 7 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 introduces FedModule, an open-sourced federated learning (FL) experimental framework that supports diverse FL paradigms and provides comprehensive benchmarks for complex experimental scenarios. The framework adheres to the “one code, all scenarios” principle and employs a modular design that breaks the FL process into individual components, allowing for the seamless integration of different FL paradigms. FedModule supports synchronous, asynchronous, and personalized federated learning with over 20 implemented algorithms. Experiments conducted on public datasets demonstrate the flexibility and extensibility of FedModule. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FedModule is a new tool that helps researchers study how machines can learn together without sharing their private data. It’s like a Lego set for building different types of machine learning models. The tool lets users pick from many different ways to do federated learning, including different types of communication and data sharing. This makes it easier to test and compare different ideas. The paper shows that FedModule is better than other tools in this area. |
Keywords
» Artificial intelligence » Federated learning » Machine learning
