  For frequency division duplex systems, the essential downlink channel state
information (CSI) feedback includes the links of compression, feedback,
decompression and reconstruction to reduce the feedback overhead. One efficient
CSI feedback method is the Auto-Encoder (AE) structure based on deep learning,
yet facing problems in actual deployments, such as selecting the deployment
mode when deploying in a cell with multiple complex scenarios. Rather than
designing an AE network with huge complexity to deal with CSI of all scenarios,
a more realistic mode is to divide the CSI dataset by region/scenario and use
multiple relatively simple AE networks to handle subregions' CSI. However, both
require high memory capacity for user equipment (UE) and are not suitable for
low-level devices. In this paper, we propose a new user-friendly-designed
framework based on the latter multi-tasking mode. Via Multi-Task Learning, our
framework, Single-encoder-to-Multiple-decoders (S-to-M), designs the multiple
independent AEs into a joint architecture: a shared encoder corresponds to
multiple task-specific decoders. We also complete our framework with GateNet as
a classifier to enable the base station autonomously select the right
task-specific decoder corresponding to the subregion. Experiments on the
simulating multi-scenario CSI dataset demonstrate our proposed S-to-M's
advantages over the other benchmark modes, i.e., significantly reducing the
model complexity and the UE's memory consumption
