A tutorial on federated learning from theory to practice: Foundations, software frameworks, exemplary use cases, and selected trends
MV Luzón, N Rodríguez-Barroso… - IEEE/CAA Journal of …, 2024 - ieeexplore.ieee.org
When data privacy is imposed as a necessity, Federated learning (FL) emerges as a
relevant artificial intelligence field for developing machine learning (ML) models in a …
relevant artificial intelligence field for developing machine learning (ML) models in a …
SLMFed: A stage-based and layer-wise mechanism for incremental federated learning to assist dynamic and ubiquitous IoT
Along with the vast application of Internet of Things (IoT) and the ever-growing concerns
about data protection, a novel type of learning, named incremental federated learning (IFL) …
about data protection, a novel type of learning, named incremental federated learning (IFL) …
AFL-DCS: An asynchronous federated learning framework with dynamic client scheduling
R Zhang, W Luo, Y Luo, H Zhang, J Wang - Engineering Applications of …, 2024 - Elsevier
The emerging federated learning is a distributed machine learning paradigm which enables
training a global model on a massive number of edge devices while protecting the privacy of …
training a global model on a massive number of edge devices while protecting the privacy of …
FUSE: a federated learning and U-shape split learning-based electricity theft detection framework
Conclusion In this study, we propose a novel theft detection framework named FUSE. Firstly,
we introduce a new variant of split learning named three-tier U-shape split learning into the …
we introduce a new variant of split learning named three-tier U-shape split learning into the …
A novel buffered federated learning framework for privacy-driven anomaly detection in IIoT
Industrial Internet of Things (IIoT) is highly sensitive to data privacy and cybersecurity
threats. Federated Learning (FL) has emerged as a solution for preserving privacy, enabling …
threats. Federated Learning (FL) has emerged as a solution for preserving privacy, enabling …
Adaptive Semi-Asynchronous Federated Learning over Wireless Networks
Owing to the heterogeneous computation and communication capabilities among clients,
the synchronous model aggregation in wireless federated learning (FL) is susceptible to the …
the synchronous model aggregation in wireless federated learning (FL) is susceptible to the …
Totoro: A Scalable Federated Learning Engine for the Edge
Federated Learning (FL) is an emerging distributed machine learning (ML) technique that
enables in-situ model training and inference on decentralized edge devices. We propose …
enables in-situ model training and inference on decentralized edge devices. We propose …
Ferrari: A personalized federated learning framework for heterogeneous edge clients
Federated semi-supervised learning (FSSL) has been proposed to address the insufficient
labeled data problem by training models with pseudo-labeling. In previous FSSL systems, a …
labeled data problem by training models with pseudo-labeling. In previous FSSL systems, a …
T-FedHA: A Trusted Hierarchical Asynchronous Federated Learning Framework for Internet of Things
Y Cao, D Liu, S Zhang, T Wu, F Xue, H Tang - Expert Systems with …, 2024 - Elsevier
Federated Learning (FL) is a distributed machine learning system designed to effectively
address potential data privacy concerns, making it particularly promising for the Internet of …
address potential data privacy concerns, making it particularly promising for the Internet of …
PI-Fed: Continual Federated Learning With Parameter-Level Importance Aggregation
Federated Learning (FL) has drawn much attention for distributed system over the Internet of
Things (IoT), since it enables collaborative machine learning on heterogeneous devices …
Things (IoT), since it enables collaborative machine learning on heterogeneous devices …