The success of machine learning (ML) techniques in the formerly difficult areas of data
analysis and pattern extraction has led to their widespread incorporation into various …

Machine learning (ML) has succeeded in improving our daily routines by enabling
automation and improved decision making in a variety of industries such as healthcare …

Federated Learning (FL) can protect the privacy of the vehicles in vehicle edge computing
(VEC) to a certain extent through sharing the gradients of vehicles' local models instead of …

In the traditional distributed machine learning scenario, the user's private data is transmitted
between clients and a central server, which results in significant potential privacy risks. In …

Federated learning is a distributed machine learning approach that enables a large number
of edge/end devices to perform on-device training for a single machine learning model …

Does Federated Learning (FL) work when both uplink and downlink communications have
errors? How much communication noise can FL handle and what is its impact on the …

Federated learning is a distributed learning paradigm where multiple agents, each only with
access to local data, jointly learn a global model. There has recently been an explosion of …

Federated Learning (FL) enhances predictive accuracy in load forecasting by integrating
data from distributed load networks while ensuring data privacy. However, the …

Nowadays, Federated Learning (FL) over Internet of Medical Things (IoMT) devices has
become a current research hotspot. As a new architecture, FL can well protect the data …

With the growing concerns on data security and user privacy, a decentralized mechanism is
implemented for federated data mining (FDM), which can bridge data silos and collaborate …