Summary of Decentralized Directed Collaboration For Personalized Federated Learning, by Yingqi Liu et al.
Decentralized Directed Collaboration for Personalized Federated Learning
by Yingqi Liu, Yifan Shi, Qinglun Li, Baoyuan Wu, Xueqian Wang, Li Shen
First submitted to arxiv on: 28 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC); Optimization and Control (math.OC)
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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 paper proposes a decentralized approach to personalized federated learning called Decentralized Federated Partial Gradient Push (DFedPGP). This method allows for distributed model training in a peer-to-peer manner, addressing issues with heterogeneity in data, computation, and communication resources. The proposed framework incorporates stochastic gradient push and partial model personalization to achieve better convergence rates and personalized performance. The paper theoretically shows that DFedPGP achieves a superior convergence rate of O(1/√T) in the general non-convex setting and proves that tighter connectivity among clients speeds up convergence. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to learn from many devices or computers at once, while keeping their personal data private. This is done by having each device learn from its own data, but still share information with others in a decentralized way. This approach helps address the problem of unequal resources (data, computation, and communication) among devices. The new method, called Decentralized Federated Partial Gradient Push, uses ideas like gradient push and partial model personalization to achieve better results. It can learn faster and more accurately than previous methods. |
Keywords
* Artificial intelligence * Federated learning
