Summary of Feddw: Distilling Weights Through Consistency Optimization in Heterogeneous Federated Learning, by Jiayu Liu et al.
FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning
by Jiayu Liu, Yong Wang, Nianbin Wang, Jing Yang, Xiaohui Tao
First submitted to arxiv on: 5 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Engineering, Finance, and Science (cs.CE)
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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 novel framework for Federated Learning (FL) called FedDW, which addresses the challenges of data heterogeneity and increasing network scale in FL. By identifying and regularizing consistencies in neural networks, FedDW outperforms 10 state-of-the-art FL methods with an average improvement of 3% accuracy in highly heterogeneous settings. The framework also offers higher efficiency, with minimal additional computational load. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated Learning is a way to train artificial intelligence models on many devices without sharing their private data. This helps protect people’s privacy, but it can be tricky because the data might not be the same across all devices. To make it work better, researchers have found that some neural networks have built-in rules or patterns that help them learn from different types of data. The paper proposes a new approach to use these rules to improve how FL models are trained, and shows that it works well in experiments. |
Keywords
» Artificial intelligence » Federated learning
