Summary of Bridging Gaps: Federated Multi-view Clustering in Heterogeneous Hybrid Views, by Xinyue Chen et al.
Bridging Gaps: Federated Multi-View Clustering in Heterogeneous Hybrid Views
by Xinyue Chen, Yazhou Ren, Jie Xu, Fangfei Lin, Xiaorong Pu, Yang Yang
First submitted to arxiv on: 12 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 multi-view clustering (FedMVC) is a technique for analyzing data distributed across multiple devices. Existing methods assume all clients have the same type of data or belong to single-view or multi-view categories. However, this paper addresses limitations by introducing a novel FedMVC framework that handles heterogeneous hybrid views with varying degrees of heterogeneity. The proposed approach involves two key components: local-synergistic contrastive learning and global-specific weighting aggregation. The former aims to reduce client gaps by promoting consistency among single-view and multi-view clients, while the latter addresses view gaps by encouraging global models to learn complementary features from hybrid views. This interplay enhances exploration of data cluster structures distributed across multiple clients. Theoretical analysis and experiments demonstrate the effectiveness of this method in outperforming state-of-the-art approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a lot of different kinds of data, like pictures or text files, stored on many devices. Normally, computers are either just storing simple information (like text) or more complex things (like images). But what if some devices store both simple and complex types of data? This can make it hard to find patterns in the data. A new way to deal with this problem is called federated multi-view clustering (FedMVC). The main idea is to make computers work together, even if they have different kinds of data, so that we can better understand what’s going on. |
Keywords
» Artificial intelligence » Clustering
