Summary of Efficient Collaborations Through Weight-driven Coalition Dynamics in Federated Learning Systems, by Mohammed El Hanjri et al.
Efficient Collaborations through Weight-Driven Coalition Dynamics in Federated Learning Systems
by Mohammed El Hanjri, Hamza Reguieg, Adil Attiaoui, Amine Abouaomar, Abdellatif Kobbane, Mohamed El Kamili
First submitted to arxiv on: 22 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC); Computer Science and Game Theory (cs.GT)
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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 proposes a decentralized machine learning framework that leverages the Euclidean distance between device model weights to facilitate coalition formation among devices with similar models. The approach relies on the concept of a barycenter, which represents the average of model weights, to aggregate updates from multiple devices. Experimental results demonstrate the efficacy of this method in offering structured and communication-efficient models for IoT-based machine learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using special math to help connected devices learn together without sharing their information. The idea is that devices with similar “models” (like a blueprint) can work together, and a new concept called a “barycenter” helps them share updates. This could be useful for smart homes or cities where many devices need to learn from each other. |
Keywords
* Artificial intelligence * Euclidean distance * Machine learning
