Summary of The Dog Walking Theory: Rethinking Convergence in Federated Learning, by Kun Zhai et al.
The Dog Walking Theory: Rethinking Convergence in Federated Learning
by Kun Zhai, Yifeng Gao, Xingjun Ma, Difan Zou, Guangnan Ye, Yu-Gang Jiang
First submitted to arxiv on: 18 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 Federated learning algorithm, called FedWalk, addresses convergence issues in collaborative learning by introducing a novel concept, the “Dog Walking Theory”. This framework formulates the missing element in existing research: the leash that guides client exploration. By leveraging an external easy-to-converge task as a leash task at the server side, FedWalk theoretically analyzes its convergence with respect to data heterogeneity and task discrepancy. Empirical results on multiple benchmark datasets demonstrate the superiority of FedWalk over state-of-the-art FL methods in both IID and non-IID settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning allows different devices to train one powerful model without sharing their private data. However, it suffers from convergence issues due to differences in data distribution. A new concept called “Dog Walking Theory” helps solve this problem. It compares the process of a dog walker with multiple dogs to FL. The server is like the dog walker and clients are like dogs. This analogy highlights a crucial missing element: the leash that guides client exploration. A novel algorithm, FedWalk, uses an external task as a leash to guide local training. Tests on multiple datasets show that FedWalk works better than other methods in both equal and unequal data settings. |
Keywords
» Artificial intelligence » Federated learning
