Federated learning (FL), as a disruptive machine learning (ML) paradigm, enables the
collaborative training of a global model over decentralized local data sets without sharing …

To further preserve the data privacy of federated learning (FL), we propose a differentially
private FL (DPFL) scheme with misaligned power allocation (MPA-DPFL). Unlike most …

Federated learning (FL) enables edge devices to collaboratively train machine learning
models, with model communication replacing direct data uploading. While over-the-air …

In conventional federated learning (FL), differential privacy (DP) guarantees can be obtained
by injecting additional noise to local model updates before transmitting to the parameter …

Empowered by 5G, it has been extensively explored by existing works on the deployment of
differentially private federated learning (DPFL) in the Industrial Internet of Things (IIoT) …

Differential privacy (DP) enables model training with a guaranteed bound on privacy
leakage, therefore is widely adopted in federated learning (FL) to protect the model update …

Federated learning (FL), which enables multiple distributed devices (clients) to
collaboratively train a global model without transmitting their private data, has attracted …

With the proliferation of smart devices having built-in sensors, Internet connectivity, and
programmable computation capability in the era of Internet of things (IoT), tremendous data …

Federated learning (FL) can achieve privacy-safe and reliable collaborative training without
collecting users' private data. Its excellent privacy security potential promotes a wide range …

Federated learning (FL) is an emerging technique to support privacy-aware and resource-
constrained machine learning, where a base station (BS) will coordinate a set of distributed …