Summary of Improved Generalization Bounds For Communication Efficient Federated Learning, by Peyman Gholami et al.
Improved Generalization Bounds for Communication Efficient Federated Learning
by Peyman Gholami, Hulya Seferoglu
First submitted to arxiv on: 17 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 research proposes a novel approach to reducing the communication cost in federated learning by exploring generalization bounds and representation learning. By characterizing tighter generalization bounds for one-round and multi-round federated learning, the authors show that less frequent aggregations can lead to more generalizable models, particularly in non-iid scenarios. The study designs a new algorithm, FedALS, which adapts local steps based on the generalization bound and representation learning analysis. Experimental results demonstrate the effectiveness of FedALS in reducing communication cost. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make learning together with many devices easier by studying how to make models better at handling different types of data. The researchers found that when they let some parts of a model learn from more data, it gets better at working with different kinds of information. They created an algorithm called FedALS that uses this idea and makes it work faster and more efficiently. This can help devices share less information while still learning together. |
Keywords
» Artificial intelligence » Federated learning » Generalization » Representation learning
