AI RESEARCH
FedPrism: Adaptive Personalized Federated Learning under Non-IID Data
arXiv CS.LG
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ArXi:2603.08252v1 Announce Type: new Federated Learning (FL) suffers significant performance degradation in real-world deployments characterized by moderate to extreme statistical heterogeneity (non-IID client data). While global aggregation strategies promote broad generalization, they often fail to capture the diversity of local data distributions, leading to suboptimal personalization. We address this problem with FedPrism, a framework that uses two main strategies.