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FedAWA: Adaptive Optimization of Aggregation Weights in Federated Learning Using Client Vectors
Federated Learning (FL) has emerged as a promising framework for distributed machine learning, enabling collaborative model training without sharing local data, thereby preserving privacy and enhancing security. However, data heterogeneity resulting from differences across user behav-iors, preferenc
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