All Outputs (5)

A Preliminary Scoping Study of Federated Learning for the Internet of Medical Things (2021)
Book Chapter
Farhad, A., Woolley, S. I., & Andras, P. (2021). A Preliminary Scoping Study of Federated Learning for the Internet of Medical Things. In J. Mantas, L. Stoicu-Tivadar, C. Chronaki, A. Hasman, P. Weber, P. Gallos, …O. Sorina Chirila (Eds.), Public Health

This paper presents a scoping review of federated learning for the Internet of Medical Things (IoMT) and demonstrates the limited amount of research work in an area which has potential to improve patient care. Federated Learning and IoMT – as standal... Read More about A Preliminary Scoping Study of Federated Learning for the Internet of Medical Things.

Compounding barriers to fairness in the digital technology ecosystem (2021)
Presentation / Conference Contribution
Woolley, S. I., Collins, T., Andras, P., Gardner, A., Ortolani, M., & Pitt, J. (2021). Compounding barriers to fairness in the digital technology ecosystem. In 2021 IEEE International Symposium on Technology and Society (ISTAS). https://doi.org/10.1109/i

A growing sense of unfairness permeates our quasi-digital society. Despite drivers supporting and motivating ethical practice in the digital technology ecosystem, there are compounding barriers to fairness that, at every level, impact technology inno... Read More about Compounding barriers to fairness in the digital technology ecosystem.

A review of privacy-preserving federated learning for the Internet-of-Things (2021)
Book Chapter
Briggs, C., Fan, Z., & Andras, P. (2021). A review of privacy-preserving federated learning for the Internet-of-Things. In M. Habib ur Rehman, & M. Medhat Gaber (Eds.), Federated Learning Systems: Towards Next-Generation AI (21-50). Cham: Springer. https:

The Internet-of-Things (IoT) generates vast quantities of data. Much of this data is attributable to human activities and behavior. Collecting personal data and executing machine learning tasks on this data in a central location presents a significan... Read More about A review of privacy-preserving federated learning for the Internet-of-Things.

Where do successful populations originate from? (2021)
Journal Article
Andras, P., & Stanton, A. (2021). Where do successful populations originate from?. Journal of Theoretical Biology, 524, Article 110734. https://doi.org/10.1016/j.jtbi.2021.110734

In order to understand the dynamics of emergence and spreading of socio-technical innovations and population moves it is important to determine the place of origin of these populations. Here we focus on the role of geographical factors, such as land... Read More about Where do successful populations originate from?.

Federated Learning for Short-term Residential Energy Demand Forecasting (2021)
Preprint / Working Paper
Briggs, C., Fan, Z., & Andras, P. (2021). Federated Learning for Short-term Residential Energy Demand Forecasting

Energy demand forecasting is an essential task performed within the energy industry to help balance supply with demand and maintain a stable load on the electricity grid. As supply transitions towards less reliable renewable energy generation, smart... Read More about Federated Learning for Short-term Residential Energy Demand Forecasting.