Summary of Aiding Global Convergence in Federated Learning Via Local Perturbation and Mutual Similarity Information, by Emanuel Buttaci et al.
Aiding Global Convergence in Federated Learning via Local Perturbation and Mutual Similarity Information
by Emanuel Buttaci, Giuseppe Carlo Calafiore
First submitted to arxiv on: 7 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC)
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 In this paper, researchers propose a novel federated learning framework that leverages the statistical similarity between clients to optimize model training. The framework is based on conceiving the federated network as a similarity graph and uses perturbed gradient steps with prior information about other statistically affine clients. The authors theoretically prove that their approach achieves a quantifiable speedup compared to popular algorithms like FedAvg and FedProx in strongly convex cases. Experimental results on CIFAR10 and FEMNIST datasets show that the algorithm speeds up convergence by up to 30 global rounds while improving generalization on unseen data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has developed a new way for devices to work together to train machine learning models without sharing their data. This is called federated learning, and it’s important because more devices are being made that can do complex calculations. The new method uses information about how similar each device’s data is to others to make the training process faster and better. The authors tested this method on two datasets and found that it worked up to 30 times faster than other methods while also doing a good job of generalizing to new, unseen data. |
Keywords
» Artificial intelligence » Federated learning » Generalization » Machine learning
