Summary of Equitable Federated Learning with Activation Clustering, by Antesh Upadhyay and Abolfazl Hashemi
Equitable Federated Learning with Activation Clustering
by Antesh Upadhyay, Abolfazl Hashemi
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Signal Processing (eess.SP)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposes a novel framework for federated learning that addresses the issue of bias in distributed machine learning. The traditional approach often overlooks the heterogeneity among clients, leading to perpetuation of bias towards certain groups. To mitigate this, the authors suggest an equitable clustering-based strategy, which categorizes clients based on their similarity using activation vectors. The framework also includes a client weighing mechanism to ensure equal importance for each cluster. Experimental results demonstrate the effectiveness of the proposed method in reducing bias among various client clusters and improving algorithmic fairness. The paper contributes to the development of more equitable and privacy-preserving distributed machine learning algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure that when we use machines to learn from different people’s data, it doesn’t unfairly favor one group over another. Right now, some methods don’t consider how different these groups are, which can lead to unfair results. The authors suggest a new way of grouping similar clients together and giving each group an equal say in the learning process. They test this method and show that it helps reduce bias and makes the algorithm more fair overall. |
Keywords
» Artificial intelligence » Clustering » Federated learning » Machine learning
