Results 1 to 10 of about 51,509 (109)

Joint Client Selection and CPU Frequency Control in Wireless Federated Learning Networks with Power Constraints [PDF]

Entropy, 2023
Federated learning (FL) represents a distributed machine learning approach that eliminates the necessity of transmitting privacy-sensitive local training samples.
Zhaohui Zhou, Shijie Shi, Fasong Wang, Yanbin Zhang, Yitong Li   +4 more
doaj   +2 more sources

Client Selection in Federated Learning under Imperfections in Environment

AI, 2022
Federated learning promises an elegant solution for learning global models across distributed and privacy-protected datasets. However, challenges related to skewed data distribution, limited computational and communication resources, data poisoning, and ...
Sumit Rai, Arti Kumari, Dilip K. Prasad
doaj   +3 more sources

Latency-Aware Semi-Synchronous Client Selection and Model Aggregation for Wireless Federated Learning

Future Internet, 2023
Federated learning (FL) is a collaborative machine-learning (ML) framework particularly suited for ML models requiring numerous training samples, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Random Forest, in the ...
Liangkun Yu, Xiang Sun, Rana Albelaihi, Chen Yi   +3 more
doaj   +3 more sources

Differentially Private Client Selection and Resource Allocation in Federated Learning for Medical Applications Using Graph Neural Networks [PDF]

Sensors
Federated learning (FL) has emerged as a pivotal paradigm for training machine learning models across decentralized devices while maintaining data privacy.
Sotirios C. Messinis, Nicholas E. Protonotarios, Nikolaos Doulamis   +2 more
doaj   +2 more sources

TCS-FEEL: Topology-Optimized Federated Edge Learning with Client Selection [PDF]

Sensors
Federated learning (FL) enables distributed model training across sensor-equipped edge devices while preserving data privacy. However, its performance is often hindered by statistical heterogeneity among clients and system heterogeneity in dynamic ...
Hui Chen, He Li
doaj   +2 more sources

APCSMA: Adaptive Personalized Client-Selection and Model-Aggregation Algorithm for Federated Learning in Edge Computing Scenarios [PDF]

Entropy
With the rapid advancement of the Internet and big data technologies, traditional centralized machine learning methods are challenged when dealing with large-scale datasets.
Xueting Ma, Guorui Ma, Yang Liu, Shuhan Qi   +3 more
doaj   +2 more sources

Mobility Prediction and Resource-Aware Client Selection for Federated Learning in IoT

Future Internet
This paper presents the Mobility-Aware Client Selection (MACS) strategy, developed to address the challenges associated with client mobility in Federated Learning (FL).
Rana Albelaihi
doaj   +3 more sources

A Comprehensive Overview of IoT-Based Federated Learning: Focusing on Client Selection Methods [PDF]

Sensors, 2023
Yang-Wai Chow, Naghmeh Khajehali
exaly   +2 more sources

Towards Client Selection in Satellite Federated Learning

Applied Sciences
Large-scale low Earth orbit (LEO) remote satellite constellations have become a brand new, massive source of space data. Federated learning (FL) is considered a promising distributed machine learning technology that can communicate optimally using these ...
Changhao Wu, Siyang He, Zengshan Yin, Chongbin Guo   +3 more
doaj   +3 more sources

Joint Client Selection and Receive Beamforming for Over-the-Air Federated Learning With Energy Harvesting

IEEE Open Journal of the Communications Society, 2023
Federated learning (FL) is a well-regarded distributed machine learning technology that leverages local computing resources while protecting privacy.
Caijuan Chen, Yi-Han Chiang, Hai Lin, John C.S. Lui, Yusheng Ji   +4 more
doaj   +1 more source