Federated Aggregation

Federated aggregation focuses on collaboratively training machine learning models across decentralized datasets while preserving data privacy. Current research emphasizes improving communication efficiency through techniques like frequency-space transformations and selective aggregation, addressing challenges posed by data heterogeneity and model size. This area is crucial for enabling large-scale collaborative learning in sensitive domains like healthcare and finance, where data sharing is restricted, and for enhancing the efficiency and robustness of distributed machine learning systems.