112 citations · 137 across the 7 of their papers we have counts for
7 papers
Adaptive Aggregation For Federated Learning
K. R. Jayaram, Vinod Muthusamy, Gegi Thomas +2
Advances in federated learning (FL) algorithms,along with technologies like differential privacy and homomorphic encryption, have led to FL being increasingly adopted and used in m…
Towards an Accountable and Reproducible Federated Learning: A FactSheets Approach
Nathalie Baracaldo, Ali Anwar, Mark Purcell +8
Federated Learning (FL) is a novel paradigm for the shared training of models based on decentralized and private data. With respect to ethical guidelines, FL is promising regarding…
Knowledge- and Data-driven Services for Energy Systems using Graph Neural Networks
Francesco Fusco, Bradley Eck, Robert Gormally +2
The transition away from carbon-based energy sources poses several challenges for the operation of electricity distribution systems. Increasing shares of distributed energy resourc…
IBM Federated Learning: an Enterprise Framework White Paper V0.1
Heiko Ludwig, Nathalie Baracaldo, Gegi Thomas +21
Federated Learning (FL) is an approach to conduct machine learning without centralizing training data in a single place, for reasons of privacy, confidentiality or data volume. How…
Scalable Deployment of AI Time-series Models for IoT
Bradley Eck, Francesco Fusco, Robert Gormally +2
IBM Research Castor, a cloud-native system for managing and deploying large numbers of AI time-series models in IoT applications, is described. Modelling code templates, in Python…
AI Modelling and Time-series Forecasting Systems for Trading Energy Flexibility in Distribution Grids
Bradley Eck, Francesco Fusco, Robert Gormally +2
We demonstrate progress on the deployment of two sets of technologies to support distribution grid operators integrating high shares of renewable energy sources, based on a market…