Summary of Covariances For Free: Exploiting Mean Distributions For Federated Learning with Pre-trained Models, by Dipam Goswami et al.
Covariances for Free: Exploiting Mean Distributions for Federated Learning with Pre-Trained Models
by Dipam Goswami, Simone Magistri, Kai Wang, Bartłomiej Twardowski, Andrew D. Bagdanov, Joost van de Weijer
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 This paper proposes a training-free method for federated learning, which leverages an unbiased estimator of class covariance matrices to improve performance. The approach utilizes only first-order statistics, such as class means, communicated by clients to the server, reducing communication costs by a significant fraction compared to methods relying on second-order statistics. The proposed method, called FedCOF, demonstrates improved performance in the range of 4-26% when compared to state-of-the-art methods sharing only class means, while maintaining the same communication cost. Moreover, FedCOF achieves competitive or superior performance with significantly less communication overhead. Furthermore, the authors demonstrate that initializing classifiers with FedCOF and then performing federated fine-tuning yields better and faster convergence. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to improve how computers learn together without sharing all their data. It’s like a team project where everyone contributes their piece of work, but they don’t have to share everything. The new method, called FedCOF, helps the computers learn better and faster by using some special statistics. This method is really good at finding patterns in the data, which makes it useful for many applications. It’s also much faster than other methods that require sharing more information. |
Keywords
» Artificial intelligence » Federated learning » Fine tuning
