Summary of Balancing Similarity and Complementarity For Federated Learning, by Kunda Yan et al.
Balancing Similarity and Complementarity for Federated Learning
by Kunda Yan, Sen Cui, Abudukelimu Wuerkaixi, Jingfeng Zhang, Bo Han, Gang Niu, Masashi Sugiyama, Changshui Zhang
First submitted to arxiv on: 16 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
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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 Federated Learning (FL) is crucial for mobile and IoT systems, as it enables data utilization while maintaining user privacy. A significant challenge in FL is managing statistical heterogeneity, which arises from numerous clients with diverse data sources. This requires strategic cooperation, often involving clients with similar characteristics. However, our research suggests that optimal cooperation does not necessarily involve partnering with the most similar clients. Instead, leveraging complementary data can lead to significant model performance improvements. We introduce a novel framework, FedSaC, which balances similarity and complementarity in FL cooperation by optimizing a weighted sum of model similarity and feature complementarity. Our comprehensive experiments demonstrate that FedSaC surpasses state-of-the-art FL methods in various unimodal and multimodal scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated Learning is important for keeping data private on mobile devices and the internet of things (IoT). One problem with this technology is that different devices have different types of data. This makes it hard to combine their data effectively. Our research shows that the best way to solve this problem isn’t by working with the most similar devices, but by combining data that is very different. We created a new framework called FedSaC that helps devices work together more effectively. It balances how similar or different their data is and makes sure they are all working together well. Our tests show that this framework works much better than other methods in many situations. |
Keywords
» Artificial intelligence » Federated learning
