  Federated learning ensures the privacy of clients by conducting distributed
training on individual client devices and sharing only the model weights with a
central server. However, in real-world scenarios, the heterogeneity of data
among clients necessitates appropriate personalization methods. In this paper,
we aim to address this heterogeneity using a form of parameter decoupling known
as representation learning. Representation learning divides deep learning
models into 'base' and 'head' components. The base component, capturing common
features across all clients, is shared with the server, while the head
component, capturing unique features specific to individual clients, remains
local. We propose a new representation learning-based approach that suggests
decoupling the entire deep learning model into more densely divided parts with
the application of suitable scheduling methods, which can benefit not only data
heterogeneity but also class heterogeneity. In this paper, we compare and
analyze two layer scheduling approaches, namely forward (\textit{Vanilla}) and
backward (\textit{Anti}), in the context of data and class heterogeneity among
clients. Our experimental results show that the proposed algorithm, when
compared to existing personalized federated learning algorithms, achieves
increased accuracy, especially under challenging conditions, while reducing
computation costs.
