Covariances for Free: Exploiting Mean Distributions for Federated Learning with Pre-Trained Models

• URL: http://arxiv.org/abs/2412.14326v2
• Date: Tue, 04 Feb 2025 15:11:28 GMT
• Title: Covariances for Free: Exploiting Mean Distributions for Federated Learning with Pre-Trained Models
• Authors: Dipam Goswami, Simone Magistri, Kai Wang, Bartłomiej Twardowski, Andrew D. Bagdanov, Joost van de Weijer,
• Abstract summary: Recent works have investigated the use of first-order statistics and second-order statistics to aggregate local client data distributions at the server. We propose a training-free method based on an unbiased estimator of class covariance matrices. Our method, which only uses first-order statistics in the form of class means communicated by clients to the server, incurs only a fraction of the communication costs required by methods based on communicating second-order statistics.
• Score: 19.74434265098346
• License:
• Abstract: Using pre-trained models has been found to reduce the effect of data heterogeneity and speed up federated learning algorithms. Recent works have investigated the use of first-order statistics and second-order statistics to aggregate local client data distributions at the server and achieve very high performance without any training. In this work we propose a training-free method based on an unbiased estimator of class covariance matrices. Our method, which only uses first-order statistics in the form of class means communicated by clients to the server, incurs only a fraction of the communication costs required by methods based on communicating second-order statistics. We show how these estimated class covariances can be used to initialize a linear classifier, thus exploiting the covariances without actually sharing them. When compared to state-of-the-art methods which also share only class means, our approach improves performance in the range of 4-26\% with exactly the same communication cost. Moreover, our method achieves performance competitive or superior to sharing second-order statistics with dramatically less communication overhead. Finally, using our method to initialize classifiers and then performing federated fine-tuning yields better and faster convergence. Code is available at https://github.com/dipamgoswami/FedCOF.

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