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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.
Security and Privacy Issues and Solutions in Federated Learning for Digital Healthcare
The advent of Federated Learning has enabled the creation of a high-performing model as if it had been trained on a considerable amount of data.
FedCC: Robust Federated Learning against Model Poisoning Attacks
The experiment results on FedCC demonstrate that it mitigates untargeted and targeted model poisoning or backdoor attacks while also being effective in non-Independently and Identically Distributed data environments.
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.
Federated Learning: Issues in Medical Application
Since the federated learning, which makes AI learning possible without moving local data around, was introduced by google in 2017 it has been actively studied particularly in the field of medicine.
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.
