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Communication-efficient distributed learning: An overview
Distributed learning is envisioned as the bedrock of next-generation intelligent networks,
where intelligent agents, such as mobile devices, robots, and sensors, exchange information …
where intelligent agents, such as mobile devices, robots, and sensors, exchange information …
A survey of recent advances in optimization methods for wireless communications
Mathematical optimization is now widely regarded as an indispensable modeling and
solution tool for the design of wireless communications systems. While optimization has …
solution tool for the design of wireless communications systems. While optimization has …
Applications of distributed machine learning for the internet-of-things: A comprehensive survey
The emergence of new services and applications in emerging wireless networks (eg,
beyond 5G and 6G) has shown a growing demand for the usage of artificial intelligence (AI) …
beyond 5G and 6G) has shown a growing demand for the usage of artificial intelligence (AI) …
Efficient federated learning for metaverse via dynamic user selection, gradient quantization and resource allocation
Metaverse is envisioned to merge the actual world with a virtual world to bring users
unprecedented immersive feelings. To ensure user experience, federated learning (FL) has …
unprecedented immersive feelings. To ensure user experience, federated learning (FL) has …
Distributed deep reinforcement learning based gradient quantization for federated learning enabled vehicle edge computing
Federated Learning (FL) can protect the privacy of the vehicles in vehicle edge computing
(VEC) to a certain extent through sharing the gradients of vehicles' local models instead of …
(VEC) to a certain extent through sharing the gradients of vehicles' local models instead of …
Relay-assisted federated edge learning: performance analysis and system optimization
In this paper, we study a relay-assisted federated edge learning (FEEL) network under
latency and bandwidth constraints. In this network, users collaboratively train a global model …
latency and bandwidth constraints. In this network, users collaboratively train a global model …
Why batch normalization damage federated learning on non-iid data?
As a promising distributed learning paradigm, federated learning (FL) involves training deep
neural network (DNN) models at the network edge while protecting the privacy of the edge …
neural network (DNN) models at the network edge while protecting the privacy of the edge …
Joint device selection and power control for wireless federated learning
This paper studies the joint device selection and power control scheme for wireless
federated learning (FL), considering both the downlink and uplink communications between …
federated learning (FL), considering both the downlink and uplink communications between …
Massive digital over-the-air computation for communication-efficient federated edge learning
Over-the-air computation (AirComp) is a promising technology converging communication
and computation over wireless networks, which can be particularly effective in model …
and computation over wireless networks, which can be particularly effective in model …
Computation offloading and quantization schemes for federated satellite-ground graph networks
Satellite-Ground integrated networks (SGINs) are regarded as promising network
architecture, which can provide global coverage, large broadband and mega access …
architecture, which can provide global coverage, large broadband and mega access …