Summary of Benchmarking Federated Strategies in Peer-to-peer Federated Learning For Biomedical Data, by Jose L. Salmeron et al.
Benchmarking federated strategies in Peer-to-Peer Federated learning for biomedical data
by Jose L. Salmeron, Irina Arévalo, Antonio Ruiz-Celma
First submitted to arxiv on: 15 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 In this paper, researchers investigate alternative aggregation strategies for federated learning, a distributed AI approach that enables model construction across multiple parties holding private data. Building on initial centralized architectures, they propose and test various peer-to-peer methods, including weighted averaging with different factors and participant contribution-based approaches. Their experiments use biomedical datasets to compare the performance of these strategies, finding that accuracy-weighted averages outperform traditional federated averaging. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning is a way for many people or organizations to work together on a machine learning project without sharing their private data. The researchers are trying to find better ways to combine all this information into one model. They’re looking at different methods to do this, like giving more weight to the most accurate results. They tested these methods using real medical data and found that one method was better than another. |
Keywords
* Artificial intelligence * Federated learning * Machine learning
