Summary of Riemannian Federated Learning Via Averaging Gradient Stream, by Zhenwei Huang et al.
Riemannian Federated Learning via Averaging Gradient Stream
by Zhenwei Huang, Wen Huang, Pratik Jawanpuria, Bamdev Mishra
First submitted to arxiv on: 11 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 proposed Riemannian Federated Averaging Gradient Stream (RFedAGS) algorithm is a generalization of Federated Averaging (FedAvg), designed for problems defined on a Riemannian manifold. By leveraging standard assumptions, the convergence rate of RFedAGS with fixed step sizes is proven to be sublinear for an approximate stationary solution. Additionally, global convergence is established when using decaying step sizes. Furthermore, it is shown that under the Riemannian Polyak-Łojasiewicz property, the optimal gaps generated by RFedAGS with fixed step size are linearly decreasing up to a tiny upper bound. If decaying step sizes are used, then the gaps sublinearly vanish. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to learn together on the internet is developed in this paper. It’s called Riemannian Federated Averaging Gradient Stream (RFedAGS) and it helps with problems that involve shapes or curves. The researchers proved that RFedAGS works well for a certain type of problem, which is good news! They also showed that the algorithm can find better solutions if you use the right steps. This means that RFedAGS could be used in real-life situations to help people learn and work together. |
Keywords
» Artificial intelligence » Generalization
