One big challenge to federated learning is the non-IID data distribution caused by
imbalanced classes. Existing federated learning approaches tend to bias towards classes …

Federated learning (FL) allows multiple clients to collectively train a high-performance
global model without sharing their private data. However, the key challenge in federated …

Federated learning aims to learn a global model collaboratively while the training data
belongs to different clients and is not allowed to be exchanged. However, the statistical …

Federated learning (FL) is an emerging technique used to collaboratively train a global
machine learning model while keeping the data localized on the user devices. The main …

Conventional federated learning (FL) approaches are ineffective in scenarios where clients
have significant differences in the distributions of their local data. The Non-IID data …

A central challenge in training classification models in the real-world federated system is
learning with non-IID data. To cope with this, most of the existing works involve enforcing …

Federated Learning (FL) can learn a global model across decentralized data over different
clients. However, it is susceptible to statistical heterogeneity of client-specific data. Clients …

Federated learning (FL) is an emerging machine learning paradigm where multiple
distributed clients collaboratively train a model without centrally collecting their raw data. In …

Federated learning (FL) enables multiple clients to jointly train high-performance deep
learning models while maintaining the training data locally. However, it is challenging to …

Since data is presented long-tailed in reality, it is challenging for Federated Learning (FL) to
train across decentralized clients as practical applications. We present Global-Regularized …