Summary of Fluke: Federated Learning Utility Framework For Experimentation and Research, by Mirko Polato
fluke: Federated Learning Utility frameworK for Experimentation and research
by Mirko Polato
First submitted to arxiv on: 20 Dec 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 FLuke, a Python package designed to simplify the development of new Federated Learning (FL) algorithms. Existing frameworks are inflexible, requiring researchers to implement algorithms from scratch, including baselines and experiments. FLuke is specifically designed for prototyping purposes, meant for researchers or practitioners focusing on learning components of federated systems. It’s open-source and can be used out-of-the-box or extended with new algorithms with minimal overhead. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated Learning (FL) helps computers learn together without sharing all their data. Researchers are trying to make FL better, but they often have to start from scratch. This paper creates a special tool called FLuke that makes it easier for researchers to try out new ideas and test them quickly. FLuke is free and open-source, so anyone can use it. |
Keywords
» Artificial intelligence » Federated learning
