Summary of A-fedpd: Aligning Dual-drift Is All Federated Primal-dual Learning Needs, by Yan Sun et al.
A-FedPD: Aligning Dual-Drift is All Federated Primal-Dual Learning Needs
by Yan Sun, Li Shen, Dacheng Tao
First submitted to arxiv on: 27 Sep 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 As federated learning (FL) continues to grow as a means of balancing data privacy and collaborative training, researchers have been working to optimize the process for handling large, heterogeneous datasets on edge clients. To tackle bandwidth limitations and security concerns, FL splits problems into smaller subproblems that can be solved in parallel, allowing for primal dual solutions with significant application values. This paper reviews recent developments in classical federated primal dual methods but highlights a key issue: “dual drift” caused by dual hysteresis on inactive clients under partial participation training. To address this problem, the authors propose Aligned Federated Primal Dual (A-FedPD), which constructs virtual updates to align global consensus and local variables for unparticipated clients. The A-FedPD method is analyzed for optimization and generalization efficiency on smooth non-convex objectives, showing high efficiency and practicality. Extensive experiments validate the effectiveness of this new approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning helps people share data while keeping it private. It does this by breaking down big problems into smaller ones that can be solved together. But there’s a problem: some devices don’t participate in training, which causes issues. To fix this, researchers developed Aligned Federated Primal Dual (A-FedPD), which makes sure all devices are on the same page. The method is tested and shown to work well. |
Keywords
» Artificial intelligence » Federated learning » Generalization » Optimization
