Semantics Alignment via Split Learning for Resilient Multi-User Semantic
  Communication
Jinhyuk Choi Jihong Park Seung-Woo Ko Jinho Choi Mehdi Bennis Seong-Lyun Kim
Recent studies on semantic communication commonly rely on neural network (NN)
based transceivers such as deep joint source and channel coding (DeepJSCC).
Unlike traditional transceivers, these neural transceivers are trainable using
actual source data and channels, enabling them to extract and communicate
semantics. On the flip side, each neural transceiver is inherently biased
towards specific source data and channels, making different transceivers
difficult to understand intended semantics, particularly upon their initial
encounter. To align semantics over multiple neural transceivers, we propose a
distributed learning based solution, which leverages split learning (SL) and
partial NN fine-tuning techniques. In this method, referred to as SL with layer
freezing (SLF), each encoder downloads a misaligned decoder, and locally
fine-tunes a fraction of these encoder-decoder NN layers. By adjusting this
fraction, SLF controls computing and communication costs. Simulation results
confirm the effectiveness of SLF in aligning semantics under different source
data and channel dissimilarities, in terms of classification accuracy,
reconstruction errors, and recovery time for comprehending intended semantics
from misalignment.