Summary of Adaptive Guidance For Local Training in Heterogeneous Federated Learning, by Jianqing Zhang et al.
Adaptive Guidance for Local Training in Heterogeneous Federated Learning
by Jianqing Zhang, Yang Liu, Yang Hua, Jian Cao, Qiang Yang
First submitted to arxiv on: 9 Oct 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 paper proposes Federated Learning-to-Guide (FedL2G), a method to adaptively guide local training in federated learning scenarios with diverse model architectures. By incorporating an extra objective alongside the original local objective, FedL2G ensures alignment and achieves non-convex convergence at O(1/T). The approach utilizes only first-order derivatives w.r.t. model parameters, making it efficient. Experiments across 14 heterogeneous models and two data settings demonstrate that FedL2G outperforms seven state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning is a way to let different devices or computers share information without sharing their own data. This helps keep personal information private. The problem is that these devices might have very different ways of processing this information, making it hard to work together. A new method called FedL2G tries to fix this by adjusting how each device learns from its own data. This makes it easier for the devices to work together and share their knowledge. In tests, FedL2G did better than other methods at doing this. |
Keywords
» Artificial intelligence » Alignment » Federated learning
