<ul><li>SEMFED is a novel semantic-aware resource-efficient federated learning framework designed for heterogeneous NLP tasks.</li><li>SEMFED incorporates a semantic-aware client selection mechanism, adaptive NLP-specific model architectures, and a communication-efficient semantic feature compression technique.</li><li>Experimental results show that SEMFED reduces communication costs by 80.5% while maintaining model accuracy above 98% on various NLP classification tasks, outperforming current FL approaches.</li><li>SEMFED effectively handles diverse client environments with varying resources, network reliability, and semantic data distributions, making it suitable for real-world federated NLP deployments.</li></ul>

SEMFED: Semantic-Aware Resource-Efficient Federated Learning for Heterogeneous NLP Tasks

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