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

TL;DR

SEMFED is a novel federated learning framework that enhances resource efficiency and semantic understanding for heterogeneous NLP tasks, significantly reducing communication costs while maintaining high accuracy.

Contribution

This paper introduces SEMFED, which uniquely combines semantic-aware client selection, adaptive NLP models, and semantic feature compression for federated NLP learning.

Findings

Achieves 80.5% reduction in communication costs.

Maintains over 98% model accuracy.

Outperforms existing federated learning methods.

Abstract

Background: Federated Learning (FL) has emerged as a promising paradigm for training machine learning models while preserving data privacy. However, applying FL to Natural Language Processing (NLP) tasks presents unique challenges due to semantic heterogeneity across clients, vocabulary mismatches, and varying resource constraints on edge devices. Objectives: This paper introduces SEMFED, a novel semantic-aware resource-efficient federated learning framework specifically designed for heterogeneous NLP tasks. Methods: SEMFED incorporates three key innovations: (1) a semantic-aware client selection mechanism that balances semantic diversity with resource constraints, (2) adaptive NLP-specific model architectures tailored to device capabilities while preserving semantic information, and (3) a communication-efficient semantic feature compression technique that significantly reduces bandwidth requirements. Results: Experimental results on various NLP classification tasks demonstrate that SEMFED achieves an 80.5% reduction in communication costs while maintaining model accuracy above 98%, outperforming state-of-the-art FL approaches. Conclusion: SEMFED effectively manages heterogeneous client environments with varying computational resources, network reliability, and semantic data distributions, making it particularly suitable for real-world federated NLP deployments.