  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.
