  Deep learning-based massive MIMO CSI feedback has received a lot of attention
in recent years. Now, there exists a plethora of CSI feedback models mostly
based on auto-encoders (AE) architecture with an encoder network at the user
equipment (UE) and a decoder network at the gNB (base station). However, these
models are trained for a single user in a single-channel scenario, making them
ineffective in multi-user scenarios with varying channels and varying encoder
models across the users. In this work, we address this problem by exploiting
the techniques of multi-task learning (MTL) in the context of massive MIMO CSI
feedback. In particular, we propose methods to jointly train the existing
models in a multi-user setting while increasing the performance of some of the
constituent models. For example, through our proposed methods, CSINet when
trained along with STNet has seen a $39\%$ increase in performance while
increasing the sum rate of the system by $0.07bps/Hz$.
