<ul><li>Federated learning (FL) is a promising paradigm in machine learning that enables collaborative model training across decentralized devices without sharing raw data.</li><li>The heterogeneous nature of local datasets in FL can cause model performance discrepancies, convergence challenges, and privacy concerns.</li><li>A novel FL framework called ClusterGuardFL is introduced, which uses dissimilarity scores, k-means clustering, and reconciliation confidence scores to assign weights to client updates.</li><li>Experimental results show that ClusterGuardFL improves model performance in diverse datasets.</li></ul>

Enhancing Federated Learning Through Secure Cluster-Weighted Client Aggregation

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