Client Scheduling in Wireless Federated Learning Based on Channel and Learning Qualities
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Wireless Communications Letters"
DOI: 10.1109/lwc.2022.3141792
Abstract: Federated learning (FL) emerges as a distributed training method in the Internet of Things (IoT), allowing participating clients to use their local data to train local models and upload parameters for global model aggregation after… read more here.
Keywords: channel learning; wireless; federated learning; scheduling wireless ... See more keywords
Federated-Learning-Based Client Scheduling for Low-Latency Wireless Communications
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Wireless Communications"
DOI: 10.1109/mwc.001.2000252
Abstract: Motivated by the ever-increasing demands for massive data processing and intelligent data analysis at the network edge, federated learning (FL), a distributed architecture for machine learning, has been introduced to enhance edge intelligence without compromising… read more here.
Keywords: client scheduling; wireless communications; federated learning; based client ... See more keywords
Cognition-Driven Semi-Synchronous Federated Learning with Entropy-Aware Client Scheduling in Heterogeneous Edge Networks
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Transactions on Cognitive Communications and Networking"
DOI: 10.1109/tccn.2025.3631050
Abstract: With rapid proliferation of mobile and embedded devices in heterogeneous communication environments, federated learning (FL) emerged as a key paradigm for enabling privacy-preserving distributed intelligence. However, conventional synchronous and asynchronous FL frameworks often suffer degraded… read more here.
Keywords: driven semi; semi synchronous; client scheduling; client ... See more keywords
Toward Dynamic Resource Allocation and Client Scheduling in Hierarchical Federated Learning: A Two-Phase Deep Reinforcement Learning Approach
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Transactions on Communications"
DOI: 10.1109/tcomm.2024.3420733
Abstract: Federated learning (FL) is a viable technique to train a shared machine learning model without sharing data. Hierarchical FL (HFL) system has yet to be studied regrading its multiple levels of energy, computation, communication, and… read more here.
Keywords: client scheduling; client; two phase; federated learning ... See more keywords
A Fairness-Guaranteed Framework for Semi-Asynchronous Federated Learning
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Transactions on Network Science and Engineering"
DOI: 10.1109/tnse.2025.3572223
Abstract: Federated Learning (FL) is a promising distributed machine learning framework that allows clients to collaboratively train a global model without data leakage. The synchronous FL suffers from the inefficient training caused by the slow-speed clients,… read more here.
Keywords: fairness guaranteed; semi asynchronous; framework; client scheduling ... See more keywords
Joint Client Scheduling and Wireless Resource Allocation for Heterogeneous Federated Edge Learning With Non-IID Data
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Transactions on Vehicular Technology"
DOI: 10.1109/tvt.2023.3333329
Abstract: Federated learning (FL) embraces the concepts of targeted data gathering and training, and it can reduce many of the systemic privacy costs and hazards associated with traditional machine learning frameworks. However, with the low latency… read more here.
Keywords: scheduling wireless; joint client; client scheduling; client ... See more keywords
Multi-Armed Bandit-Based Client Scheduling for Federated Learning
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Transactions on Wireless Communications"
DOI: 10.1109/twc.2020.3008091
Abstract: By exploiting the computing power and local data of distributed clients, federated learning (FL) features ubiquitous properties such as reduction of communication overhead and preserving data privacy. In each communication round of FL, the clients… read more here.
Keywords: multi armed; client scheduling; bandit based; federated learning ... See more keywords