FedPrism: Adaptive Personalized Federated Learning under Non-IID Data

arXiv:2603.08252v2 Announce Type: replace Abstract: 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 b...

🔗 Read more: https://arxiv.org/abs/2603.08252

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