<ul><li>Federated learning has become a promising solution for collaboration among medical institutions. However, data owned by each institution would be highly heterogeneous and the distribution is always non-independent and identical distribution (non-IID), resulting in client drift and unsatisfactory performance.</li><li>In this paper, a novel federated learning method called FedVCK (Federated learning via Valuable Condensed Knowledge) is proposed. It aims to tackle the non-IID problem within limited communication budgets effectively.</li><li>FedVCK condenses the knowledge of each client into a small dataset and enhances the condensation procedure with latent distribution constraints. It prevents unnecessary repetition of homogeneous knowledge and minimizes the frequency of communications required.</li><li>Experiments showed that FedVCK outperforms state-of-the-art methods in various medical tasks, demonstrating its non-IID robustness and communication efficiency.</li></ul>

FedVCK: Non-IID Robust and Communication-Efficient Federated Learning via Valuable Condensed Knowledge for Medical Image Analysis

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