Summary of Asynchronous Diffusion Learning with Agent Subsampling and Local Updates, by Elsa Rizk et al.
Asynchronous Diffusion Learning with Agent Subsampling and Local Updates
by Elsa Rizk, Kun Yuan, Ali H. Sayed
First submitted to arxiv on: 8 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Multiagent Systems (cs.MA)
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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 The proposed algorithm enables a network of agents to collaboratively learn from individual local datasets without requiring real-time communication. Each agent decides when to participate and which subset of its neighbors to cooperate with, performing multiple updates before sharing results. The algorithm is proved to be stable in the mean-square error sense, offering performance guarantees for federated learning settings. Numerical simulations demonstrate the effectiveness of this asynchronous diffusion strategy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a group of scientists working together to solve a problem. They each have their own piece of information and need to share it with others who have similar data. The challenge is that they can’t all talk at once, so they have to decide when to share and which friends to ask for help. Our algorithm helps them do this efficiently, ensuring the results are accurate and reliable. |
Keywords
* Artificial intelligence * Diffusion * Federated learning
