Search Results for author: Jed Mills

Found 5 papers, 3 papers with code

Faster Federated Learning with Decaying Number of Local SGD Steps

no code implementations16 May 2023 Jed Mills, Jia Hu, Geyong Min

FedAvg can improve the communication-efficiency of training by performing more steps of Stochastic Gradient Descent (SGD) on clients in each round.

Federated Learning

Federated Ensemble Model-based Reinforcement Learning in Edge Computing

no code implementations12 Sep 2021 Jin Wang, Jia Hu, Jed Mills, Geyong Min, Ming Xia

Federated learning (FL) is a privacy-preserving distributed machine learning paradigm that enables collaborative training among geographically distributed and heterogeneous devices without gathering their data.

Autonomous Driving Continuous Control +8

Accelerating Federated Learning with a Global Biased Optimiser

1 code implementation20 Aug 2021 Jed Mills, Jia Hu, Geyong Min, Rui Jin, Siwei Zheng, Jin Wang

To address this challenge, we propose a novel, generalised approach for incorporating adaptive optimisation into FL with the Federated Global Biased Optimiser (FedGBO) algorithm.

Federated Learning

Multi-Task Federated Learning for Personalised Deep Neural Networks in Edge Computing

1 code implementation17 Jul 2020 Jed Mills, Jia Hu, Geyong Min

MTFL is compatible with popular iterative FL optimisation algorithms such as Federated Averaging (FedAvg), and we show empirically that a distributed form of Adam optimisation (FedAvg-Adam) benefits convergence speed even further when used as the optimisation strategy within MTFL.

Edge-computing Federated Learning

Communication-Efficient Federated Learning for Wireless Edge Intelligence in IoT

1 code implementation1 Jul 2020 Jed Mills, Jia Hu, Geyong Min

The rapidly expanding number of Internet of Things (IoT) devices is generating huge quantities of data, but public concern over data privacy means users are apprehensive to send data to a central server for machine learning (ML) purposes.

Edge-computing Federated Learning

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