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Communication-Efficient Federated Learning via Fractional-Order Gradient Descent With Adaptive Momentum Under Non-IID Data

Federated learning (FL) enables collaborative model training without centralized data sharing, yet its practical deployment is often hindered by slow convergence and excessive communication overhead, particularly under non-Independent and non-Identically Distributed (non-IID) data distributions. To address these challe...

M. Hasan, Yong Xiang, Md. Palash Uddin et al. · 0 citations

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