Toward Privacy Preservation in Federated Learning: A Framework Integrating Client-Side Shuffling and Model Compression
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Federated learning (FL) enables multiple clients to train models on local data and collaboratively optimize a global model without sharing raw data. However, client heterogeneity, such as differences in data distributions and system capabilities, poses challen... ...
