FedFixer: A Machine Learning Algorithm with the Dual Model Structure to Mitigate the Impact of Heterogeneous Noisy Label Samples in Federated Learning

In today’s world, where data is distributed across various locations and privacy is paramount, Federated Learning (FL) has emerged as a game-changing solution. It enables multiple parties to train machine learning models collaboratively without sharing their data, ensuring that sensitive information remains locally stored and protected. However, a significant challenge arises when the data labels […]

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