Standard machine-learning approaches involve the centralization of training data in a data center, where centralized machine-learning algorithms can be applied for data analysis and …
W Shi, S Zhou, Z Niu, M Jiang… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
In federated learning (FL), devices contribute to the global training by uploading their local model updates via wireless channels. Due to limited computation and communication …
L Fu, H Zhang, G Gao, M Zhang… - IEEE Internet of Things …, 2023 - ieeexplore.ieee.org
As a privacy-preserving paradigm for training machine learning (ML) models, federated learning (FL) has received tremendous attention from both industry and academia. In a …
HT Nguyen, V Sehwag… - IEEE Journal on …, 2020 - ieeexplore.ieee.org
Federated learning has emerged recently as a promising solution for distributing machine learning tasks through modern networks of mobile devices. Recent studies have obtained …
Federated learning is an emerging machine learning paradigm where clients train models locally and formulate a global model based on the local model updates. To identify the state …
The sixth generation (6G) wireless systems are envisioned to enable the paradigm shift from “connected things” to “connected intelligence”, featured by ultra high density, large-scale …
To satisfy the expected plethora of computation-heavy applications, federated edge learning (FEEL) is a new paradigm featuring distributed learning to carry the capacities of low-latency …
The federated learning technique (FL) supports the collaborative training of machine learning and deep learning models for edge network optimization. Although a complex edge …
H Wu, P Wang - IEEE Transactions on Network Science and …, 2022 - ieeexplore.ieee.org
Federated Learning (FL) is a distributed learning paradigm that enables a large number of resource-limited nodes to collaboratively train a model without data sharing. The non …