Multi-node communication, which refers to the interaction among multiple devices, has attracted lots of attention in many Internet-of-Things (IoT) scenarios. However, its huge amounts of data flows and inflexibility for task extension have triggered the urgent requirement of communication-efficient distributed data transmission frameworks. In this paper, inspired by the great superiorities on bandwidth reduction and task adaptation of semantic communications, we propose a federated learning-based semantic communication (FLSC) framework for multi-task distributed image transmission with IoT devices. Federated learning enables the design of independent semantic communication link of each user while further improves the semantic extraction and task performance through global aggregation. Each link in FLSC is composed of a hierarchical vision transformer (HVT)-based extractor and a task-adaptive translator for coarse-to-fine semantic extraction and meaning translation according to specific tasks. In order to extend the FLSC into more realistic conditions, we design a channel state information-based multiple-input multiple-output transmission module to combat channel fading and noise. Simulation results show that the coarse semantic information can deal with a range of image-level tasks. Moreover, especially in low signal-to-noise ratio and channel bandwidth ratio regimes, FLSC evidently outperforms the traditional scheme, e.g. about 10 peak signal-to-noise ratio gain in the 3 dB channel condition.

翻译：多节点通信，即多个设备之间的交互，已在许多物联网场景中引起广泛关注。然而，其庞大的数据流量及任务扩展的灵活性不足，迫切需求一种通信高效的分布式数据传输框架。受语义通信在带宽缩减和任务适应性方面的显著优势启发，本文提出一种基于联邦学习的语义通信框架（FLSC），用于物联网设备的多任务分布式图像传输。联邦学习使每个用户能够独立设计语义通信链路，同时通过全局聚合进一步提升语义提取与任务性能。FLSC中的每条链路包含基于分层视觉Transformer（HVT）的提取器与任务自适应转换器，可针对特定任务实现从粗到细的语义提取与含义转换。为将FLSC扩展至更实际的环境，我们设计了一种基于信道状态信息的多输入多输出传输模块，以对抗信道衰落与噪声。仿真结果表明，粗粒度语义信息能够处理一系列图像级任务。此外，尤其在低信噪比与信道带宽比场景下，FLSC显著优于传统方案，例如在3 dB信道条件下实现了约10 dB的峰值信噪比增益。