Summary of Distribution-free Fair Federated Learning with Small Samples, by Qichuan Yin et al.
Distribution-Free Fair Federated Learning with Small Samples
by Qichuan Yin, Zexian Wang, Junzhou Huang, Huaxiu Yao, Linjun Zhang
First submitted to arxiv on: 25 Feb 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Computers and Society (cs.CY); Machine Learning (cs.LG)
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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 addresses the pressing need for ensuring fairness in federated learning applications by introducing a new algorithm called FedFaiREE. This post-processing approach is designed specifically for decentralized settings with small samples, accounting for unique challenges like client heterogeneity and communication costs. The authors provide theoretical guarantees for both fairness and accuracy, as well as empirical validation through experimental results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make sure that machine learning models are fair when they’re trained using data from lots of different places. Right now, most methods for ensuring fairness only work if all the data is together in one place. But what if we want to train a model using small amounts of data from many different sources? We need new ways to ensure fairness in these situations. This paper introduces a new method called FedFaiREE that can do this. It’s tested and shown to be fair and accurate, even when working with limited data. |
Keywords
* Artificial intelligence * Federated learning * Machine learning
