Self-balancing federated learning with global imbalanced data in mobile systems
Federated learning (FL) is a distributed deep learning method that enables multiple
participants, such as mobile and IoT devices, to contribute a neural network while their …
participants, such as mobile and IoT devices, to contribute a neural network while their …
Communication-efficient federated learning with binary neural networks
Federated learning (FL) is a privacy-preserving machine learning setting that enables many
devices to jointly train a shared global model without the need to reveal their data to a …
devices to jointly train a shared global model without the need to reveal their data to a …
Astraea: Self-balancing federated learning for improving classification accuracy of mobile deep learning applications
Federated learning (FL) is a distributed deep learning method which enables multiple
participants, such as mobile phones and IoT devices, to contribute a neural network model …
participants, such as mobile phones and IoT devices, to contribute a neural network model …
CEFL: Online admission control, data scheduling, and accuracy tuning for cost-efficient federated learning across edge nodes
With the proliferation of Internet of Things (IoT), zillions of bytes of data are generated at the
network edge, incurring an urgent need to push the frontiers of artificial intelligence (AI) to …
network edge, incurring an urgent need to push the frontiers of artificial intelligence (AI) to …
Fedmask: Joint computation and communication-efficient personalized federated learning via heterogeneous masking
Recent advancements in deep neural networks (DNN) enabled various mobile deep
learning applications. However, it is technically challenging to locally train a DNN model due …
learning applications. However, it is technically challenging to locally train a DNN model due …
Intelligent reflecting surface-assisted low-latency federated learning over wireless networks
Federated learning (FL) is an emerging technique to support privacy-aware and resource-
constrained machine learning, where a base station (BS) will coordinate a set of distributed …
constrained machine learning, where a base station (BS) will coordinate a set of distributed …
Topology-aware federated learning in edge computing: A comprehensive survey
The ultra-low latency requirements of 5G/6G applications and privacy constraints call for
distributed machine learning systems to be deployed at the edge. With its simple yet …
distributed machine learning systems to be deployed at the edge. With its simple yet …
D2D-assisted federated learning in mobile edge computing networks
X Zhang, Y Liu, J Liu, A Argyriou… - 2021 IEEE Wireless …, 2021 - ieeexplore.ieee.org
With the proliferation of edge intelligence and the breakthroughs in machine learning,
Federated Learning (FL) is capable of learning a shared model across several edge devices …
Federated Learning (FL) is capable of learning a shared model across several edge devices …
Fedzip: A compression framework for communication-efficient federated learning
A Malekijoo, MJ Fadaeieslam, H Malekijou… - arXiv preprint arXiv …, 2021 - arxiv.org
Federated Learning marks a turning point in the implementation of decentralized machine
learning (especially deep learning) for wireless devices by protecting users' privacy and …
learning (especially deep learning) for wireless devices by protecting users' privacy and …
Toward energy-efficient distributed federated learning for 6G networks
The provision of communication services via portable and mobile devices, such as aerial
base stations, is a crucial concept to be realized in 5G/6G networks. Conventionally …
base stations, is a crucial concept to be realized in 5G/6G networks. Conventionally …