Summary of A Review on Different Techniques Used to Combat the Non-iid and Heterogeneous Nature Of Data in Fl, by Venkataraman Natarajan Iyer
A review on different techniques used to combat the non-IID and heterogeneous nature of data in FL
by Venkataraman Natarajan Iyer
First submitted to arxiv on: 1 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 delve into the challenges of Federated Learning (FL) in decentralized edge devices. FL enables collaborative model training without sharing local data samples, crucial for industries like healthcare and finance that prioritize data privacy. The team investigates the issues arising from non-independently and non-identically distributed (non-IID) and heterogeneous data, which hinder model convergence. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated Learning is a way for computers to work together without sharing their personal information. It’s super important in fields like healthcare and finance where people don’t want their private data shared. The problem is that the computers have different types of data, making it hard for the models to agree. This paper looks at why this happens and what we can do about it. |
Keywords
* Artificial intelligence * Federated learning
