Summary of Swarm Learning: a Survey Of Concepts, Applications, and Trends, by Elham Shammar et al.
Swarm Learning: A Survey of Concepts, Applications, and Trends
by Elham Shammar, Xiaohui Cui, Mohammed A. A. Al-qaness
First submitted to arxiv on: 1 May 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 This paper explores swarm learning (SL), a decentralized machine learning framework that leverages blockchain technology for secure data management. Building on federated learning (FL), SL reduces central dependency and increases scalability by enabling model parameter exchange among participants. The authors highlight the benefits of SL in addressing privacy and security concerns, particularly in the context of increasing Internet of Things (IoT) devices. They also introduce the architectural design and potential applications of SL, emphasizing the need for further research to unlock its full potential. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to do artificial intelligence called swarm learning. It’s like a team effort where computers work together to learn and share information securely. This is important because we have lots of devices connected to the internet, and we need to make sure our data stays private. The authors are trying to figure out how this works and what it can be used for. |
Keywords
» Artificial intelligence » Federated learning » Machine learning
