Summary of Relaxed Contrastive Learning For Federated Learning, by Seonguk Seo et al.
Relaxed Contrastive Learning for Federated Learning
by Seonguk Seo, Jinkyu Kim, Geeho Kim, Bohyung Han
First submitted to arxiv on: 10 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel contrastive learning framework to address data heterogeneity in federated learning. The authors analyze the inconsistency of gradient updates across clients during local training and derive the supervised contrastive learning (SCL) objective to mitigate local deviations. However, they show that naively adopting SCL in federated learning leads to representation collapse, resulting in slow convergence and limited performance gains. To address this issue, the authors introduce a relaxed contrastive learning loss that imposes a divergence penalty on excessively similar sample pairs within each class. This strategy prevents collapsed representations and enhances feature transferability, facilitating collaborative training and leading to significant performance improvements. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning is a way for devices to work together without sharing their data. Imagine you want to train an AI model to recognize different types of animals, but the pictures are taken with different cameras or lighting conditions. This can make it hard for the model to learn. The researchers propose a new way to make this process more effective by using something called contrastive learning. They show that if devices use this method alone, it won’t work very well because the model will become too similar and lose its ability to recognize different animals. To fix this, they introduce a new penalty that helps the model stay diverse and learn from the different data sources. |
Keywords
* Artificial intelligence * Federated learning * Supervised * Transferability