Background

I am currently undertaking PhD research funded under the Smart Energy Network Demonstrator (SEND) project and the School of Computing and Mathematics at Keele university. I am researching the intersection of privacy preserving machine learning and smart energy applications under the supervision of Prof Zhong Fan and Prof Peter Andras.

Research interests

My main research focus is on privacy preseving machine learning for smart energy applications. I amparticularly interested in a form of collaborative machine learning over distributed data known as federated learning. Although federated learning demonstrates a promising way forward to provide private analytics over sensitive user data, the protocol suffers when data is not ideally distributed between the connected users. My research focuses on training multiple specialised models that acheive good performance for subsets of users by clustering users whoprovide similar model updates to the global model. As part of the SEND project, I aim to apply my work to forecasting energy consumption in a privacy preserving way using deep learning.

Additional to my main research focus, I have worked on several projects in/outside of the university such as:

Lead organiser of the OpenMined Privacy Conference 2020
Scoping review on user-centric design for smart local energy systems (Zero Carbon Rugeley project
E-MISSION: a multiplayer text-based game to increase awareness of lifestyle choices’ impact on our planet
Deep learning classification of giant cell tumors in X-ray images of long bones
Learning analytics and automated module evaluation in higher education
Tracking of books within the university library using low-energy LoraWAN and RFID technologies

Recent publications & conferences

Briggs, C., Fan, Z., Andras, P. (2022) Federated Learning for Short-term Residential Energy Demand Forecasting. IEEE Open Access Journal of Power and Energy, vol. 9, pp. 573-583 Link Public preprint
Briggs, C., Fan, Z., Andras, P. (2021) A Review of Privacy-preserving Federated Learning for the Internet-of-Things. Federated Learning Systems: Towards Next Generation AI in Springer's Series on Studies in Computational Intelligence Link Public preprint
Briggs, C., Fan, Z., Andras, P. (2020) Privacy Preserving Demand Forecasting to Encourage Consumer Acceptance of Smart Energy Meters. Tackling Climate Change with Machine Learning wokrkshop at NeurIPS 2020 Link Public preprint
Briggs, C., Fan, Z., Andras, P. (2020) Federated Learning with Hierarchical Clustering of Local Updates to Improve Training on Non-IID Data. Proceedings of 2020 International Joint Conference on Neural Networks (IJCNN) Link Public preprint
de Quincey, E., Briggs, C., Kyriacou, T. & Waller R. (2019) Student Centred Design of a Learning Analytics System. Proceedings of the 9th International Conference on Learning Analytics & Knowledge (LAK '19). pp. 353-362 Link
de Quincey, E. & Briggs, C. (2018) Learner Centred Design of Learning Analytics. QAA Good practice case study Link
de Quincey, E., Briggs, C. & Mitchell, J. (2018) Co-designing, Developing and Integrating a Student Facing Learning Analytics Systems. 16th Academic Practice and Technology Conference (apt2018) Link
Briggs, S.J., Cage, A.G. & Briggs, C. (2017). How Do UK Environmental Publishers Use Facebook to Engage Users with Sustainability?. Meliora: International Journal of Student Sustainability Research. 1(1) Link