Applications of distributed machine learning for the Internet-of-Things: A comprehensive survey
The emergence of new services and applications in emerging wireless networks (eg,
beyond 5G and 6G) has shown a growing demand for the usage of artificial intelligence (AI) …
beyond 5G and 6G) has shown a growing demand for the usage of artificial intelligence (AI) …
Semi-federated learning for collaborative intelligence in massive IoT networks
W Ni, J Zheng, H Tian - IEEE Internet of Things Journal, 2023 - ieeexplore.ieee.org
Implementing existing federated learning in massive Internet of Things (IoT) networks faces
critical challenges, such as imbalanced and statistically heterogeneous data and device …
critical challenges, such as imbalanced and statistically heterogeneous data and device …
Accelerating hybrid federated learning convergence under partial participation
Over the past few years, Federated Learning (FL) has become a popular distributed
machine learning paradigm. FL involves a group of clients with decentralized data who …
machine learning paradigm. FL involves a group of clients with decentralized data who …
Accelerating wireless federated learning with adaptive scheduling over heterogeneous devices
As the proliferation of sophisticated task models in 5G-empowered digital twin, it yields
significant demands on fast and accurate model training over resource-limited wireless …
significant demands on fast and accurate model training over resource-limited wireless …
Hybrid Learning: When Centralized Learning Meets Federated Learning in the Mobile Edge Computing Systems
Federated learning is a new artificial intelligence technology with which an edge server can
orchestrate with multiple end users to train a global model collaboratively. Under this setting …
orchestrate with multiple end users to train a global model collaboratively. Under this setting …
Dynamic Resource Management for Federated Edge Learning With Imperfect CSI: A Deep Reinforcement Learning Approach
S Zhou, L Feng, M Mei, M Yao - IEEE Internet of Things Journal, 2024 - ieeexplore.ieee.org
Federated edge learning (FEL) has become a research hotspot to relieve the computational
burden on servers and protect users' data privacy. In an FEL system, adjusting the client …
burden on servers and protect users' data privacy. In an FEL system, adjusting the client …
REWAFL: Residual Energy and Wireless Aware Participant Selection for Efficient Federated Learning Over Mobile Devices
Participant selection (PS) helps to accelerate federated learning (FL) convergence, which is
essential for the practical deployment of FL over mobile devices. While most existing PS …
essential for the practical deployment of FL over mobile devices. While most existing PS …
Fed-EE: Federating Heterogeneous ASR Models using Early-Exit Architectures
MNAM Nawar, D Falavigna, A Brutti - Proceedings of 3rd Neurips …, 2023 - cris.fbk.eu
Automatic speech recognition models require large speech recordings for training. However,
the collection of such data often is cumbersome and leads to privacy concerns. Federated …
the collection of such data often is cumbersome and leads to privacy concerns. Federated …
Collaborative Optimization of Wireless Communication and Computing Resource Allocation based on Multi-Agent Federated Weighting Deep Reinforcement Learning
J Wu, X Fang - arXiv preprint arXiv:2404.01638, 2024 - arxiv.org
As artificial intelligence (AI)-enabled wireless communication systems continue their
evolution, distributed learning has gained widespread attention for its ability to offer …
evolution, distributed learning has gained widespread attention for its ability to offer …
Convergence Analysis and Latency Minimization for Retransmission-Based Semi-Federated Learning
In this paper, we propose a semi-federated learning (SemiFL) framework to ameliorate the
performance of conventional federated learning. The base station and devices are …
performance of conventional federated learning. The base station and devices are …