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，已在众多物联网场景中引起广泛关注。然而，其庞大的数据流和任务扩展的灵活性不足，催生了对高效通信分布式数据传输框架的迫切需求。本文受语义通信在带宽压缩和任务适应性方面的显著优势启发，提出了一种基于联邦学习的语义通信框架，用于物联网设备的多任务分布式图像传输。联邦学习使得每个用户能够独立设计其语义通信链路，同时通过全局聚合进一步提升语义提取和任务性能。该框架中的每条链路由基于分层视觉Transformer的提取器和任务自适应翻译器组成，用于根据特定任务实现从粗到细的语义提取和意义翻译。为将所提框架扩展到更实际的条件，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，以对抗信道衰落和噪声。仿真结果表明，粗语义信息可处理一系列图像级任务。此外，尤其在低信噪比和低信道带宽比条件下，所提框架明显优于传统方案，例如在3dB信道条件下可获得约10分贝的峰值信噪比增益。