Heterogeneous Federated Semantic Communication for Time Series Forecasting
Bowen Zhao, Huanlai Xing, Lexi Xu, Danyang Zheng, Jincheng Peng, Zhiwen Xiao, Li Feng · 2024
This paper studies a distributed semantic communication (SC) problem for multivariate time series forecasting tasks in edge environments, with heterogeneous clients considered. At the client side, a semantic encoder is composed of a number of federated blocks and this number is subject to local resource availability. Each federated block consists of a patch-wise attention module (PAM) and a federated adapter, extracting semantic information for efficient transmission across wireless channels. Based on the federated adapters, this paper proposes an SC-oriented heterogeneous federated learning architecture, named SC-FedAda. SC-FedAda adopts self-distillation to facilitate cross-client and cross-layer knowledge sharing, enabling efficient collaborative inference. At the edge server, semantic signals are fed into a channel decoder and then a semantic decoder. The semantic decoder consists of a PAM and a fully connected network for forecasting tasks. Simulation results demonstrate that SC-FedAda outperforms four state-of-the-art federated learning-based structures under three types of wireless channels, i.e. SC-FedAda achieves much lower forecasting loss on three widely-used time series datasets, particularly in low signal-to-noise ratio scenarios.