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This project is applying federated learning techniques to Internet of Things (IoT) applications. The results could be used for forecasting energy demand in smart grids while protecting household’s raw smart meter data. The results could also be used for energy analytics, such as specific appliance demand profiles, without revealing which households participated in the dataset.
The project could have many applications to the SEND project. One example would be providing 发送 with a way to see how energy demand is likely to fluctuate within residential homes on campus in order optimise the network, while maintaining the privacy of the data from the individual households.
Project progress (June 2019):
- Submission of literature review (Feb 2019)
- Presentations and poster presentations at various Keele internal conferences and symposia (Mar 2019 – May 2019)
- Submission of review paper to ACM Transactions on Intelligent Systems & Technology (TIST) journal (Apr 2019)
- Re-implementation of state-of-the-art federated learning algorithm to recreate the results of McMahan et al 2017 (Ongoing)
商业 联系(s): James Priest
Keele Graduate 研究er: Chris Briggs