Blockchain meets federated learning in healthcare: A systematic review with challenges and opportunities
R Myrzashova, SH Alsamhi… - IEEE Internet of …, 2023 - ieeexplore.ieee.org
Recently, innovations in the Internet of Medical Things (IoMT), information and
communication technologies, and machine learning (ML) have enabled smart healthcare …
communication technologies, and machine learning (ML) have enabled smart healthcare …
[HTML][HTML] At the confluence of artificial intelligence and edge computing in iot-based applications: A review and new perspectives
Given its advantages in low latency, fast response, context-aware services, mobility, and
privacy preservation, edge computing has emerged as the key support for intelligent …
privacy preservation, edge computing has emerged as the key support for intelligent …
[HTML][HTML] Privacy-preserving federated learning for residential short-term load forecasting
With high levels of intermittent power generation and dynamic demand patterns, accurate
forecasts for residential loads have become essential. Smart meters can play an important …
forecasts for residential loads have become essential. Smart meters can play an important …
Federated learning with hyperparameter-based clustering for electrical load forecasting
N Gholizadeh, P Musilek - Internet of Things, 2022 - Elsevier
Electrical load prediction has become an integral part of power system operation. Deep
learning models have found popularity for this purpose. However, to achieve a desired …
learning models have found popularity for this purpose. However, to achieve a desired …
Federated fuzzy k-means for privacy-preserving behavior analysis in smart grids
Better understanding the behavior of various participants in smart grids, such as electricity
consumers and generators, is important and beneficial for flexibility exploration and …
consumers and generators, is important and beneficial for flexibility exploration and …
Federated learning for big data: A survey on opportunities, applications, and future directions
Big data has remarkably evolved over the last few years to realize an enormous volume of
data generated from newly emerging services and applications and a massive number of …
data generated from newly emerging services and applications and a massive number of …
[HTML][HTML] Deep federated adaptation: An adaptative residential load forecasting approach with federated learning
Y Shi, X Xu - Sensors, 2022 - mdpi.com
Residential-level short-term load forecasting (STLF) is significant for power system
operation. Data-driven forecasting models, especially machine-learning-based models, are …
operation. Data-driven forecasting models, especially machine-learning-based models, are …
[HTML][HTML] Federated learning-based multi-energy load forecasting method using CNN-Attention-LSTM model
G Zhang, S Zhu, X Bai - Sustainability, 2022 - mdpi.com
Integrated Energy Microgrid (IEM) has emerged as a critical energy utilization mechanism
for alleviating environmental and economic pressures. As a part of demand-side energy …
for alleviating environmental and economic pressures. As a part of demand-side energy …
Der forecast using privacy-preserving federated learning
V Venkataramanan, S Kaza… - IEEE Internet of Things …, 2022 - ieeexplore.ieee.org
With the increasing penetration of distributed energy resources (DERs) in grid edge,
including renewable generation, flexible loads, and storage, accurate prediction of …
including renewable generation, flexible loads, and storage, accurate prediction of …
Smart meter-based energy consumption forecasting for smart cities using adaptive federated learning
Forecasting short-term residential energy consumption is critical in modern decentralized
power systems. Deep learning-based prediction methods that can handle the high variability …
power systems. Deep learning-based prediction methods that can handle the high variability …