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SoK: Challenges and Opportunities in Federated Unlearning
This SoK paper aims to take a deep look at the \emph{federated unlearning} literature, with the goal of identifying research trends and challenges in this emerging field.
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.
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.
ABC-FL: Anomalous and Benign client Classification in Federated Learning
In this study, we propose a method that detects and classifies anomalous clients from benign clients when benign ones have non-IID 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.
