5 citations · 13 across the 4 of their papers we have counts for
4 papers
Federated Learning for Short-term Residential Load Forecasting
Christopher Briggs, Zhong Fan, Peter Andras
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 transiti…
Privacy Preserving Demand Forecasting to Encourage Consumer Acceptance of Smart Energy Meters
Christopher Briggs, Zhong Fan, Peter Andras
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 resoluti…
Federated learning with hierarchical clustering of local updates to improve training on non-IID data
Christopher Briggs, Zhong Fan, Peter Andras
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-i…
A Review of Privacy-preserving Federated Learning for the Internet-of-Things
Christopher Briggs, Zhong Fan, Peter Andras
The Internet-of-Things (IoT) generates vast quantities of data, much of it attributable to individuals' activity and behaviour. Gathering personal data and performing machine learn…