Federated learning (FL) has drawn increasing attention owing to its potential use in large-
scale industrial applications. Existing FL works mainly focus on model homogeneous …

Federated Learning (FL) enables distributed training by learners using local data, thereby
enhancing privacy and reducing communication. However, it presents numerous challenges …

In social IoMT systems, resource-constrained devices face the challenges of limited
computation, bandwidth, and privacy in the deployment of deep learning models. Federated …

Software defect prediction is used to identify modules in software projects that may have
defects. Heterogeneous Defect Prediction (HDP) establishes a cross project defect …

Federated learning has emerged as a promising paradigm for privacy-preserving
collaboration among different parties. Recently, with the popularity of federated learning, an …

This paper proposes a client selection (CS) method to tackle the communication bottleneck
of federated learning (FL) while concurrently coping with FL's data heterogeneity issue …

Federated learning facilitates collaborative model training in the autonomous IoT system
while preserving the privacy of local data on IoT clients. Nonetheless, the inherent non-IID …

Federated learning is a privacy-preserving machine learning paradigm to protect the data of
clients against privacy breaches. A lot of work on federated learning considers the cross …

Traditional Federated Learning (FL) follows a server-domincated cooperation paradigm
which narrows the application scenarios of FL and decreases the enthusiasm of data …

The proliferation of multi-media applications and increased computing power of mobile
devices have led to the development of personalized artificial intelligent (AI) applications …