A framework combining semantic-aware client selection, heterogeneous model sizes, and feature compression reports 98.5% accuracy and 80.5% less communication on a synthetic 10-client NLP task.
Advances and open problems in federated learning,
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SEMFED: Semantic-Aware Resource-Efficient Federated Learning for Heterogeneous NLP Tasks
A framework combining semantic-aware client selection, heterogeneous model sizes, and feature compression reports 98.5% accuracy and 80.5% less communication on a synthetic 10-client NLP task.