In recent years, the powerful combination of Multi-access Edge Computing (MEC) and
Artificial Intelligence (AI), called edge intelligence, promotes the development of Intelligent …

Federated learning (FL) is an efficient decentralized machine learning methodology for
processing nonindependent and identically distributed (non-IID) data due to geographical …

Efficient collaboration between collaborative machine learning and wireless communication
technology, forming a Federated Edge Learning (FEEL), has spawned a series of next …

Federated learning (FL) has become one of the mainstream paradigms for multi-party
collaborative learning with privacy protection. As it is difficult to guarantee all FL devices to …

Federated Learning is a new approach for distributed training of a deep learning model on
data scattered across a large number of clients while ensuring data privacy. However, this …

Federated learning has emerged as a promising approach for collaborative model training
across distributed devices. Federated learning faces challenges such as Non-Independent …

Federated Learning (FL) is a machine learning approach in which a cluster of clients
collaboratively trains a model without sharing the data of any clients. As the datasets of each …

The emerging paradigm of federated learning (FL) strives to enable devices to cooperatively
train models without exposing their raw data. In most cases, the data across devices are non …

Heterogeneous defect prediction (HDP) constructs a defect prediction model through the
source project to realize the defect tendency prediction of the target project. HDP based on …

Federated learning (FL) is a machine learning paradigm for data silos and privacy
protection, which aims to organize multiple clients for training global machine learning …