  Deep learning (DL)-based channel state information (CSI) feedback has
received significant research attention in recent years. However, previous
research has overlooked the potential privacy disclosure problem caused by the
transmission of CSI datasets during the training process. In this work, we
introduce a federated edge learning (FEEL)-based training framework for
DL-based CSI feedback. This approach differs from the conventional centralized
learning (CL)-based framework in which the CSI datasets are collected at the
base station (BS) before training. Instead, each user equipment (UE) trains a
local autoencoder network and exchanges model parameters with the BS. This
approach provides better protection for data privacy compared to CL. To further
reduce communication overhead in FEEL, we quantize uplink and downlink model
transmission into different bits based on their influence on feedback
performance. Additionally, since the heterogeneity of CSI datasets in different
UEs can degrade the performance of the FEEL-based framework, we introduce a
personalization strategy to improve feedback performance. This strategy allows
for local fine-tuning to adapt the global model to the channel characteristics
of each UE. Simulation results indicate that the proposed personalized
FEEL-based training framework can significantly improve the performance of
DL-based CSI feedback while reducing communication overhead.
