no code implementations • 16 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.
no code implementations • 12 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.
1 code implementation • 20 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.
1 code implementation • 17 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.
1 code implementation • 1 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.