  Federated Learning (FL) has recently emerged as a promising method that
employs a distributed learning model structure to overcome data privacy and
transmission issues paused by central machine learning models. In FL, datasets
collected from different devices or sensors are used to train local models
(clients) each of which shares its learning with a centralized model (server).
However, this distributed learning approach presents unique learning challenges
as the data used at local clients can be non-IID (Independent and Identically
Distributed) and statistically diverse which decrease learning accuracy in the
central model. In this paper, we overcome this problem by proposing a novel
Personalized Conditional FedAvg (PC-FedAvg) which aims to control weights
communication and aggregation augmented with a tailored learning algorithm to
personalize the resulting models at each client. Our experimental validation on
two datasets showed that our PC-FedAvg precisely constructed generalized
clients' models and thus achieved higher accuracy compared to other
state-of-the-art methods.
