Clustered federated learning with model integration for non-iid data in wireless networks
As a typical distributed learning paradigm, federated learning has enabled network edge
intelligence by making full use of the local data and the computing resources at edge …
intelligence by making full use of the local data and the computing resources at edge …
Federated learning with non-iid data in wireless networks
Federated learning provides a promising paradigm to enable network edge intelligence in
the future sixth generation (6G) systems. However, due to the high dynamics of wireless …
the future sixth generation (6G) systems. However, due to the high dynamics of wireless …
Computation and Communication Efficient Federated Learning over Wireless Networks
X Liu, T Ratnarajah - arXiv preprint arXiv:2309.01816, 2023 - arxiv.org
Federated learning (FL) allows model training from local data by edge devices while
preserving data privacy. However, the learning accuracy decreases due to the heterogeneity …
preserving data privacy. However, the learning accuracy decreases due to the heterogeneity …
Adaptive Clustering based Straggler-aware Federated Learning in Wireless Edge Networks
Federated learning (FL) has been vigorously promoted in wireless edge networks as it
fosters collaborative training of machine learning (ML) models while preserving individual …
fosters collaborative training of machine learning (ML) models while preserving individual …
Ensemble federated learning with non-IID data in wireless networks
Federated learning is a promising technique to implement network intelligence for the sixth
generation (6G) communication systems. However, the collected data in wireless networks …
generation (6G) communication systems. However, the collected data in wireless networks …
Robust design of federated learning for edge-intelligent networks
Mass data traffics, low-latency wireless services and advanced artificial intelligence (AI)
technologies have driven the emergence of a new paradigm for wireless networks, namely …
technologies have driven the emergence of a new paradigm for wireless networks, namely …
Clustered Data Sharing for Non-IID Federated Learning over Wireless Networks
Federated Learning (FL) is a novel distributed machine learning approach to leverage data
from Internet of Things (IoT) devices while maintaining data privacy. However, the current FL …
from Internet of Things (IoT) devices while maintaining data privacy. However, the current FL …
Discrepancy-Aware Federated Learning for Non-IID Data
J Shen, S Chen - 2023 IEEE Wireless Communications and …, 2023 - ieeexplore.ieee.org
Federated learning (FL) as an emerging edge intelligence paradigm allows clients to jointly
train a model without exchanging raw data. Due to its excellent performance in privacy …
train a model without exchanging raw data. Due to its excellent performance in privacy …
Semi-decentralized federated edge learning with data and device heterogeneity
Federated edge learning (FEEL) emerges as a privacy-preserving paradigm to effectively
train deep learning models from the distributed data in 6G networks. Nevertheless, the …
train deep learning models from the distributed data in 6G networks. Nevertheless, the …
Joint model pruning and device selection for communication-efficient federated edge learning
In recent years, wireless federated learning (FL) has been proposed to support the mobile
intelligent applications over the wireless network, which protects the data privacy and …
intelligent applications over the wireless network, which protects the data privacy and …