Federated Computing--Survey on Building Blocks, Extensions and Systems
R Schwermer, R Mayer, HA Jacobsen - arXiv preprint arXiv:2404.02779, 2024 - arxiv.org
In response to the increasing volume and sensitivity of data, traditional centralized
computing models face challenges, such as data security breaches and regulatory hurdles …
computing models face challenges, such as data security breaches and regulatory hurdles …
Secure short-term load forecasting for smart grids with transformer-based federated learning
J Sievers, T Blank - 2023 International Conference on Clean …, 2023 - ieeexplore.ieee.org
Electricity load forecasting is an essential task within smart grids to assist demand and
supply balance. While advanced deep learning models require large amounts of high …
supply balance. While advanced deep learning models require large amounts of high …
Fed‐SAD: A secure aggregation federated learning method for distributed short‐term load forecasting
H Li, S Wu, R Wang, Y Guo, J Li - … Generation, Transmission & …, 2023 - Wiley Online Library
The distributed and privacy‐preserving attributes of fine‐grained smart grid data create
obstacles to data sharing. As a result, federated learning emerges as an effective strategy for …
obstacles to data sharing. As a result, federated learning emerges as an effective strategy for …
Advancing Accuracy in Energy Forecasting using Mixture-of-Experts and Federated Learning
J Sievers, T Blank, F Simon - Proceedings of the 15th ACM International …, 2024 - dl.acm.org
Accurate forecasting of load, photovoltaic (PV), and prosumption is essential for energy
systems to reliably plan and operate smart grids, improve energy storage optimization, or …
systems to reliably plan and operate smart grids, improve energy storage optimization, or …
Federated Computing Systems In The Context of Energy Informatics
RK Schwermer - 2024 - mediatum.ub.tum.de
Federated computing (FC) is an emerging privacy-preserving computing model. It consists of
federated analytics and federated learning. Besides privacy enhancement, FC also helps …
federated analytics and federated learning. Besides privacy enhancement, FC also helps …
Advancing Electric Load Forecasting: Leveraging Federated Learning for Distributed, Non-Stationary, and Discontinuous Time Series
L Richter, S Lenk, P Bretschneider - 2024 - preprints.org
This paper addresses the evolving landscape of electricity markets in Europe, with a focus
on the integration of Renewable Energy Communities as introduced by the Renewable …
on the integration of Renewable Energy Communities as introduced by the Renewable …
Fed-SAD: A secure aggregation federated learning method for distributed load forecasting
J Li, H Li, R Wang, Y Guo, S Wu - Authorea Preprints, 2023 - essopenarchive.org
The distributed and privacy-preserving characteristics of fine-grained smart grid data hinder
data sharing, making federated learning an attractive approach for collaborative training …
data sharing, making federated learning an attractive approach for collaborative training …