| Federated learning with hierarchical clustering of local updates to improve training on non-IID data C Briggs, Z Fan, P Andras 2020 International Joint Conference on Neural Networks (IJCNN), 1-9, 2020 | 492 | 2020 |
| A review of privacy-preserving federated learning for the Internet-of-Things C Briggs, Z Fan, P Andras Federated Learning Systems: Towards Next-Generation AI, 21-50, 2021 | 78* | 2021 |
| Student centred design of a learning analytics system E de Quincey, C Briggs, T Kyriacou, R Waller Proceedings of the 9th international conference on learning analytics …, 2019 | 65 | 2019 |
| Federated learning for short-term residential load forecasting C Briggs, Z Fan, P Andras IEEE Open Access Journal of Power and Energy 9, 573-583, 2022 | 25 | 2022 |
| Federated learning for short-term residential energy demand forecasting C Briggs, Z Fan, P Andras | 24 | 2021 |
| A Review of Privacy-preserving Federated Learning for the Internet-of-Things. arXiv 2020 C Briggs, Z Fan, P Andras arXiv preprint arXiv:2004.11794, 0 | 5 | |
| Privacy preserving demand forecasting to encourage consumer acceptance of smart energy meters C Briggs, Z Fan, P Andras arXiv preprint arXiv:2012.07449, 2020 | 3 | 2020 |
| Co-designing, Developing and Integrating a Student Facing Learning Analytics Systems E de Quincey, C Briggs, J Mitchell apt2018, 2018 | | 2018 |
| How Do UK Environmental Publishers Use Facebook to Engage Users with Sustainability? C Briggs, A Cage, SJ Briggs Meliora: International Journal of Student Sustainability Research, 2017 | | 2017 |
Prof. Zhong FanProfessorVerified email at exeter.ac.uk
