<ul><li>Federated learning (FL) is a popular method that allows training deep learning models on decentralized datasets held by different clients.</li><li>Personalized FL (pFL) methods aim to address challenges related to statistical heterogeneity between distributed datasets by combining global learning and local modeling specific to each client.</li><li>A study explores the effectiveness of adaptive Maximum Mean Discrepancy (MMD) measures in the Ditto framework, a leading technique in pFL, which enhances model performance particularly in tasks with diverse feature characteristics.</li><li>The findings suggest that these adaptive MMD measures can benefit various pFL scenarios and advocate for constraints customized to the types of heterogeneity present in FL systems.</li></ul>

Adaptive Latent-Space Constraints in Personalized FL

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