Jed Mills - researchr alias

researchr

You are not signed in
Sign in
Sign up

Viewing Publication 1 - 8 from 8

2023

Lightweight Blockchain-Empowered Secure and Efficient Federated Edge LearningRui Jin, Jia Hu 0001, Geyong Min, Jed Mills. TC, 72(11):3314-3325, November 2023. [doi]

Faster Federated Learning With Decaying Number of Local SGD StepsJed Mills, Jia Hu 0001, Geyong Min. tpds, 34(7):2198-2207, July 2023. [doi]

Accelerating Federated Learning With a Global Biased OptimiserJed Mills, Jia Hu 0001, Geyong Min, Rui Jin, Siwei Zheng, Jin Wang. TC, 72(6):1804-1814, June 2023. [doi]

Federated Ensemble Model-Based Reinforcement Learning in Edge ComputingJin Wang, Jia Hu 0001, Jed Mills, Geyong Min, Ming Xia, Nektarios Georgalas. tpds, 34(6):1848-1859, June 2023. [doi]

2022

Client-Side Optimization Strategies for Communication-Efficient Federated LearningJed Mills, Jia Hu 0001, Geyong Min. cm, 60(7):60-66, 2022. [doi]

Multi-Task Federated Learning for Personalised Deep Neural Networks in Edge ComputingJed Mills, Jia Hu 0001, Geyong Min. tpds, 33(3):630-641, 2022. [doi]

2020

Communication-Efficient Federated Learning for Wireless Edge Intelligence in IoTJed Mills, Jia Hu, Geyong Min. iotj, 7(7):5986-5994, 2020. [doi]

Proactive Content Caching for Internet-of-Vehicles based on Peer-to-Peer Federated LearningZhengxin Yu, Jia Hu 0001, Geyong Min, Han Xu, Jed Mills. icpads 2020: 601-608 [doi]

Sign in or sign up to see more results.

runs on WebDSL