Resource-efficient federated learning with hierarchical aggregation in edge computing
Federated learning (FL) has emerged in edge computing to address limited bandwidth and
privacy concerns of traditional cloud-based centralized training. However, the existing FL …
privacy concerns of traditional cloud-based centralized training. However, the existing FL …
Federated learning with non-iid data
Federated learning enables resource-constrained edge compute devices, such as mobile
phones and IoT devices, to learn a shared model for prediction, while keeping the training …
phones and IoT devices, to learn a shared model for prediction, while keeping the training …
[HTML][HTML] Federated learning for 6G-enabled secure communication systems: a comprehensive survey
Abstract Machine learning (ML) and Deep learning (DL) models are popular in many areas,
from business, medicine, industries, healthcare, transportation, smart cities, and many more …
from business, medicine, industries, healthcare, transportation, smart cities, and many more …
One bit aggregation for federated edge learning with reconfigurable intelligent surface: Analysis and optimization
H Li, R Wang, W Zhang, J Wu - IEEE Transactions on Wireless …, 2022 - ieeexplore.ieee.org
As one of the most popular and attractive frameworks for model training, federated edge
learning (FEEL) presents a new paradigm, which avoids direct data transmission by …
learning (FEEL) presents a new paradigm, which avoids direct data transmission by …
Communication-efficient personalized federated meta-learning in edge networks
Due to the privacy breach risks and data aggregation of traditional centralized machine
learning (ML) approaches, applications, data and computing power are being pushed from …
learning (ML) approaches, applications, data and computing power are being pushed from …
Partialfed: Cross-domain personalized federated learning via partial initialization
The burst of applications empowered by massive data have aroused unprecedented privacy
concerns in AI society. Currently, data confidentiality protection has been one core issue …
concerns in AI society. Currently, data confidentiality protection has been one core issue …
Fedict: Federated multi-task distillation for multi-access edge computing
The growing interest in intelligent services and privacy protection for mobile devices has
given rise to the widespread application of federated learning in Multi-access Edge …
given rise to the widespread application of federated learning in Multi-access Edge …
Resource-aware federated learning using knowledge extraction and multi-model fusion
With increasing concern about user data privacy, federated learning (FL) has been
developed as a unique training paradigm for training machine learning models on edge …
developed as a unique training paradigm for training machine learning models on edge …
FEEL: A federated edge learning system for efficient and privacy-preserving mobile healthcare
With the prosperity of artificial intelligence, neural networks have been increasingly applied
in healthcare for a variety of tasks for medical diagnosis and disease prevention. Mobile …
in healthcare for a variety of tasks for medical diagnosis and disease prevention. Mobile …
Fedmix: Approximation of mixup under mean augmented federated learning
Federated learning (FL) allows edge devices to collectively learn a model without directly
sharing data within each device, thus preserving privacy and eliminating the need to store …
sharing data within each device, thus preserving privacy and eliminating the need to store …