TITLE: FedPDC:Federated Learning for Public Dataset Correction 

ABSTRACT: As people pay more and more attention to privacy protection, Federated
Learning (FL), as a promising distributed machine learning paradigm, is
receiving more and more attention. However, due to the biased distribution of
data on devices in real life, federated learning has lower classification
accuracy than traditional machine learning in Non-IID scenarios. Although there
are many optimization algorithms, the local model aggregation in the parameter
server is still relatively traditional. In this paper, a new algorithm FedPDC
is proposed to optimize the aggregation mode of local models and the loss
function of local training by using the shared data sets in some industries. In
many benchmark experiments, FedPDC can effectively improve the accuracy of the
global model in the case of extremely unbalanced data distribution, while
ensuring the privacy of the client data. At the same time, the accuracy
improvement of FedPDC does not bring additional communication costs.
As people pay more and more attention to privacy protection, Federated
Learning (FL), as a promising distributed machine learning paradigm, is
receiving more and more attention. However, due to the biased distribution of
data on devices in real life, federated learning has lower classification
accuracy than traditional machine learning in Non-IID scenarios. Although there
are many optimization algorithms, the local model aggregation in the parameter
server is still relatively traditional. In this paper, a new algorithm FedPDC
is proposed to optimize the aggregation mode of local models and the loss
function of local training by using the shared data sets in some industries. In
many benchmark experiments, FedPDC can effectively improve the accuracy of the
global model in the case of extremely unbalanced data distribution, while
ensuring the privacy of the client data. At the same time, the accuracy
improvement of FedPDC does not bring additional communication costs.
FedPDC:Federated Learning for Public Dataset Correction
http://arxiv.org/abs/2302.12503v1