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Distribution-Level AirComp for Wireless Federated Learning under Data Scarcity and Heterogeneity
Federated Learning (FL) has gained considerable attention as a privacy-preserving and localized approach to implementing edge artificial intelligence (AI). However, conventional FL methods face critical challenges in realistic wireless edge networks, where training data is both limited and heterogen
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