Decentralized federated learning: A survey and perspective
Federated learning (FL) has been gaining attention for its ability to share knowledge while
maintaining user data, protecting privacy, increasing learning efficiency, and reducing …
maintaining user data, protecting privacy, increasing learning efficiency, and reducing …
Distributed learning for wireless communications: Methods, applications and challenges
With its privacy-preserving and decentralized features, distributed learning plays an
irreplaceable role in the era of wireless networks with a plethora of smart terminals, an …
irreplaceable role in the era of wireless networks with a plethora of smart terminals, an …
[HTML][HTML] Graph neural networks for intelligent modelling in network management and orchestration: a survey on communications
The advancing applications based on machine learning and deep learning in
communication networks have been exponentially increasing in the system architectures of …
communication networks have been exponentially increasing in the system architectures of …
Accelerating decentralized federated learning in heterogeneous edge computing
In edge computing (EC), federated learning (FL) enables massive devices to collaboratively
train AI models without exposing local data. In order to avoid the possible bottleneck of the …
train AI models without exposing local data. In order to avoid the possible bottleneck of the …
Adaptive configuration for heterogeneous participants in decentralized federated learning
Data generated at the network edge can be processed locally by leveraging the paradigm of
edge computing (EC). Aided by EC, decentralized federated learning (DFL), which …
edge computing (EC). Aided by EC, decentralized federated learning (DFL), which …
Finch: Enhancing Federated Learning With Hierarchical Neural Architecture Search
Federated learning (FL) has been widely adopted to train machine learning models over
massive data in edge computing. Most works of FL employ pre-defined model architectures …
massive data in edge computing. Most works of FL employ pre-defined model architectures …
Computation and communication efficient federated learning with adaptive model pruning
Federated learning (FL) has emerged as a promising distributed learning paradigm that
enables a large number of mobile devices to cooperatively train a model without sharing …
enables a large number of mobile devices to cooperatively train a model without sharing …
Enhancing decentralized federated learning for non-iid data on heterogeneous devices
Data generated at the network edge can be processed locally by leveraging the emerging
technology of Federated Learning (FL). However, non-IID local data will lead to degradation …
technology of Federated Learning (FL). However, non-IID local data will lead to degradation …
Yoga: Adaptive layer-wise model aggregation for decentralized federated learning
Traditional Federated Learning (FL) is a promising paradigm that enables massive edge
clients to collaboratively train deep neural network (DNN) models without exposing raw data …
clients to collaboratively train deep neural network (DNN) models without exposing raw data …
Fedcd: A hybrid federated learning framework for efficient training with iot devices
With billions of Internet of Things devices producing vast data globally, privacy and efficiency
challenges arise in artificial intelligence applications. Federated learning (FL) has been …
challenges arise in artificial intelligence applications. Federated learning (FL) has been …