no code implementations • 17 May 2024 • Lorenzo Sani, Alex Iacob, Zeyu Cao, Bill Marino, Yan Gao, Tomas Paulik, Wanru Zhao, William F. Shen, Preslav Aleksandrov, Xinchi Qiu, Nicholas D. Lane
Generative pre-trained large language models (LLMs) have demonstrated impressive performance over a wide range of tasks, thanks to the unprecedented amount of data they have been trained on.
no code implementations • 15 Feb 2024 • Xinchi Qiu, Yan Gao, Lorenzo Sani, Heng Pan, Wanru Zhao, Pedro P. B. Gusmao, Mina Alibeigi, Alex Iacob, Nicholas D. Lane
Federated learning (FL) is a distributed learning paradigm that facilitates collaborative training of a shared global model across devices while keeping data localized.
no code implementations • 20 May 2023 • Alex Iacob, Pedro P. B. Gusmão, Nicholas D. Lane, Armand K. Koupai, Mohammud J. Bocus, Raúl Santos-Rodríguez, Robert J. Piechocki, Ryan McConville
This work studies the impact of privacy in federated HAR at a user, environment, and sensor level.
no code implementations • 4 May 2023 • Alex Iacob, Pedro P. B. Gusmão, Nicholas D. Lane
For situations where the federated model provides a lower accuracy than a model trained entirely locally by a client, personalisation improves the accuracy of the pre-trained federated weights to be similar to or exceed those of the local client model.