Outputs (161)

Federated Learning for Short-term Residential Load Forecasting (2022)
Journal Article
Briggs, C., Fan, Z., & Andras, P. (2022). Federated Learning for Short-term Residential Load Forecasting. IEEE Open Access Journal of Power and Energy, 9, 573-583. https://doi.org/10.1109/oajpe.2022.3206220

Load 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 meters wi... Read More about Federated Learning for Short-term Residential Load Forecasting.

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 and Informatics (504-505). Amsterdam: IOS Press. https://doi.org/10.3233/SHTI210216

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)
Conference Proceeding
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/istas52410.2021.9629166

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://doi.org/10.1007/978-3-030-70604-3_2

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.

Federated Learning for Short-term Residential Energy Demand Forecasting (2021)
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.

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?.

Privacy Preserving Demand Forecasting to Encourage Consumer Acceptance of Smart Energy Meters (2020)
Conference Proceeding
Briggs, C., Fan, Z., & Andras, P. (2020). Privacy Preserving Demand Forecasting to Encourage Consumer Acceptance of Smart Energy Meters. In NeurIPS 2020 Workshop: Tackling Climate Change with Machine Learning

In this proposal paper we highlight the need for privacy preserving energy demand forecasting to allay a major concern consumers have about smart meter installations. High resolution smart meter data can expose many private aspects of a consumer’s ho... Read More about Privacy Preserving Demand Forecasting to Encourage Consumer Acceptance of Smart Energy Meters.

Federated learning with hierarchical clustering of local updates to improve training on non-IID data (2020)
Conference Proceeding
Briggs, C., Fan, Z., & Andras, P. (2020). Federated learning with hierarchical clustering of local updates to improve training on non-IID data. In 2020 International Joint Conference on Neural Networks (IJCNN). https://doi.org/10.1109/IJCNN48605.2020.9207469

Federated learning (FL) is a well established method for performing machine learning tasks over massively distributed data. However in settings where data is distributed in a non-iid (not independent and identically distributed) fashion - as is typic... Read More about Federated learning with hierarchical clustering of local updates to improve training on non-IID data.

Composition of Games as a Model for the Evolution of Social Institutions (2020)
Conference Proceeding
Andras, P. (2020). Composition of Games as a Model for the Evolution of Social Institutions. In Artificial Life Conference Proceedings (171-179). https://doi.org/10.1162/isal_a_00264

The evolution of social institutions (e.g. institutions of political decision making or joint resource administration) is an important question in the context of understanding of how societies develop and evolve. In principle, social institutions can... Read More about Composition of Games as a Model for the Evolution of Social Institutions.

Scalability analysis comparisons of cloud-based software services (2019)
Journal Article
Ahmad, A. A., & Andras, P. (2019). Scalability analysis comparisons of cloud-based software services. Journal of cloud computing: advances, systems and applications, 8, Article 10. https://doi.org/10.1186/s13677-019-0134-y

Performance and scalability testing and measurements of cloud-based software services are necessary for future optimizations and growth of cloud computing. Scalability, elasticity, and efficiency are interrelated aspects of cloud-based software servi... Read More about Scalability analysis comparisons of cloud-based software services.