SemDA: Communication-Efficient Data Aggregation Through Distributed Semantic Transmission

Abstract

This paper introduces SemDA, a communication-efficient data aggregation method that uses distributed semantic communication for improved transmission and analysis. SemDA utilizes an end-to-end trainable network structure that reduces data transmission volume and deepens semantic feature aggregation. Key advances include an attention-based aggregation method for holistic semantic feature integration and a dual-attention decoding network that emphasizes viewpoint and content dimensions. Performance evaluations on CIFAR-10 and ImageNet datasets show that SemDA offers significant improvements in accuracy and system overhead compared to traditional and distributed semantic communication methods. Notable contributions include the proposal of a novel decoding structure, the introduction of a dual-attention decoding mechanism, and extensive evaluations against benchmark methods.