Harnessing wireless channels for scalable and privacy-preserving federated learning
Wireless connectivity is instrumental in enabling scalable federated learning (FL), yet
wireless channels bring challenges for model training, in which channel randomness …
wireless channels bring challenges for model training, in which channel randomness …
Artificial neural networks-based machine learning for wireless networks: A tutorial
In order to effectively provide ultra reliable low latency communications and pervasive
connectivity for Internet of Things (IoT) devices, next-generation wireless networks can …
connectivity for Internet of Things (IoT) devices, next-generation wireless networks can …
Energy-efficient radio resource allocation for federated edge learning
Q Zeng, Y Du, K Huang… - 2020 IEEE International …, 2020 - ieeexplore.ieee.org
Edge machine learning involves the development of learning algorithms at the network edge
to leverage massive distributed data and computation resources. Among others, the …
to leverage massive distributed data and computation resources. Among others, the …
Toward an intelligent edge: Wireless communication meets machine learning
The recent revival of AI is revolutionizing almost every branch of science and technology.
Given the ubiquitous smart mobile gadgets and IoT devices, it is expected that a majority of …
Given the ubiquitous smart mobile gadgets and IoT devices, it is expected that a majority of …
Joint parameter-and-bandwidth allocation for improving the efficiency of partitioned edge learning
To leverage data and computation capabilities of mobile devices, machine learning
algorithms are deployed at the network edge for training artificial intelligence (AI) models …
algorithms are deployed at the network edge for training artificial intelligence (AI) models …
Federated learning over wireless device-to-device networks: Algorithms and convergence analysis
The proliferation of Internet-of-Things (IoT) devices and cloud-computing applications over
siloed data centers is motivating renewed interest in the collaborative training of a shared …
siloed data centers is motivating renewed interest in the collaborative training of a shared …
Reconfigurable intelligent surface enabled federated learning: A unified communication-learning design approach
To exploit massive amounts of data generated at mobile edge networks, federated learning
(FL) has been proposed as an attractive substitute for centralized machine learning (ML). By …
(FL) has been proposed as an attractive substitute for centralized machine learning (ML). By …
Quantized federated learning under transmission delay and outage constraints
Federated learning (FL) has been recognized as a viable distributed learning paradigm
which trains a machine learning model collaboratively with massive mobile devices in the …
which trains a machine learning model collaboratively with massive mobile devices in the …
Federated learning over wireless networks: A band-limited coordinated descent approach
J Zhang, N Li, M Dedeoglu - IEEE INFOCOM 2021-IEEE …, 2021 - ieeexplore.ieee.org
We consider a many-to-one wireless architecture for federated learning at the network edge,
where multiple edge devices collaboratively train a model using local data. The unreliable …
where multiple edge devices collaboratively train a model using local data. The unreliable …
High-dimensional stochastic gradient quantization for communication-efficient edge learning
Edge machine learning involves the deployment of learning algorithms at the wireless
network edge so as to leverage massive mobile data for enabling intelligent applications …
network edge so as to leverage massive mobile data for enabling intelligent applications …