418 citations · 497 across the 6 of their papers we have counts for
6 papers
Faster Federated Learning with Decaying Number of Local SGD Steps
Jed Mills, Jia Hu, Geyong Min
In Federated Learning (FL) client devices connected over the internet collaboratively train a machine learning model without sharing their private data with a central server or wit…
Federated Ensemble Model-based Reinforcement Learning in Edge Computing
Jin Wang, Jia Hu, Jed Mills +2
Federated learning (FL) is a privacy-preserving distributed machine learning paradigm that enables collaborative training among geographically distributed and heterogeneous devices…
Accelerating Federated Learning with a Global Biased Optimiser
Jed Mills, Jia Hu, Geyong Min +3
Federated Learning (FL) is a recent development in distributed machine learning that collaboratively trains models without training data leaving client devices, preserving data pri…
Online Service Migration in Mobile Edge with Incomplete System Information: A Deep Recurrent Actor-Critic Learning Approach
Jin Wang, Jia Hu, Geyong Min +2
Multi-access Edge Computing (MEC) is an emerging computing paradigm that extends cloud computing to the network edge to support resource-intensive applications on mobile devices. A…
Fast Adaptive Task Offloading in Edge Computing based on Meta Reinforcement Learning
Jin Wang, Jia Hu, Geyong Min +2
Multi-access edge computing (MEC) aims to extend cloud service to the network edge to reduce network traffic and service latency. A fundamental problem in MEC is how to efficiently…
Multi-Task Federated Learning for Personalised Deep Neural Networks in Edge Computing
Jed Mills, Jia Hu, Geyong Min
Federated Learning (FL) is an emerging approach for collaboratively training Deep Neural Networks (DNNs) on mobile devices, without private user data leaving the devices. Previous…