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FedUV: Uniformity and Variance for Heterogeneous Federated Learning
Based on this finding, we introduce two regularization terms for local training to continuously emulate IID settings: (1) variance in the dimension-wise probability distribution of the classifier and (2) hyperspherical uniformity of representations of the encoder.
Compare Where It Matters: Using Layer-Wise Regularization To Improve Federated Learning on Heterogeneous Data
Federated Learning is a widely adopted method to train neural networks over distributed data.
Personalized Federated Learning with Clustering: Non-IID Heart Rate Variability Data Application
While machine learning techniques are being applied to various fields for their exceptional ability to find complex relations in large datasets, the strengthening of regulations on data ownership and privacy is causing increasing difficulty in its application to medical data.
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