Summary of The Diversity Bonus: Learning From Dissimilar Distributed Clients in Personalized Federated Learning, by Xinghao Wu et al.
The Diversity Bonus: Learning from Dissimilar Distributed Clients in Personalized Federated Learning
by Xinghao Wu, Xuefeng Liu, Jianwei Niu, Guogang Zhu, Shaojie Tang, Xiaotian Li, Jiannong Cao
First submitted to arxiv on: 22 Jul 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 The proposed Personalized Federated Learning (PFL) framework allows for collaborative training of personalized models among multiple clients, particularly useful when data distributions across clients are non-IID. Previous PFL approaches implicitly assume that clients benefit more from those with similar data distributions. However, this assumption is questioned in the paper, which explores whether a client can benefit from other clients with dissimilar data distributions and how. The proposed DiversiFed method enables each client to learn from others with diversified data distribution in PFL, pushing apart models of clients with dissimilar distributions while pulling together those with similar ones. A designed loss function leverages model similarity to determine the degree of attraction and repulsion between any two models. Experimental results on several datasets demonstrate that DiversiFed can benefit from dissimilar clients, outperforming state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PFL is a way for many devices to work together to improve their own personalized models. Right now, most PFL approaches assume that devices with similar data will help each other more than devices with very different data. But this paper asks: can we actually benefit from devices with very different data? If so, how do we make it happen? The proposed method, DiversiFed, allows devices to learn from others even if they have very different data. This is achieved by making models that are very different in certain situations and bringing them together when they are similar. Experimental results show that this approach can be more effective than previous methods. |
Keywords
» Artificial intelligence » Federated learning » Loss function
