Federated learning is a privacy-preserving machine learning technique that trains models
across multiple devices holding local data samples without exchanging them. There are …

The advent of federated learning (FL) has sparked a new paradigm of parallel and
confidential decentralized machine learning (ML) with the potential of utilizing the …

Centralized networks inevitably exhibit single points of failure that malicious actors regularly
target. Decentralized networks are more resilient if numerous participants contribute to the …

Unmanned aerial vehicles (UAVs) extend the traditional ground-based Internet of Things
(IoT) into the air. UAV mobile edge computing (MEC) architectures have been proposed by …

Evaluation is a systematic approach to assessing how well a system achieves its intended
purpose. Federated learning (FL) is a novel paradigm for privacy-preserving machine …

Federated learning (FL) is a type of distributed machine learning approacs that trains global
models through the collaboration of participants. It protects data privacy as participants only …

Federated learning (FL) is a machine learning approach that decentralizes data and its
processing by allowing clients to train intermediate models on their devices with locally …

Leveraging various mobile devices to train the shared model collaboratively, federated
learning (FL) can improve the privacy and security of 6G communication. To economically …

Cross-silo federated learning (CFL) is a distributed learning paradigm that allows
organizations (eg, financial or medical entities) to train a global model on siloed data …

While centralized servers pose a risk of being a single point of failure, decentralized
approaches like blockchain offer a compelling solution by implementing a consensus …