Federated learning review: Fundamentals, enabling technologies, and future applications
Federated Learning (FL) has been foundational in improving the performance of a wide
range of applications since it was first introduced by Google. Some of the most prominent …
range of applications since it was first introduced by Google. Some of the most prominent …
A comprehensive survey of privacy-preserving federated learning: A taxonomy, review, and future directions
The past four years have witnessed the rapid development of federated learning (FL).
However, new privacy concerns have also emerged during the aggregation of the …
However, new privacy concerns have also emerged during the aggregation of the …
A review of applications in federated learning
L Li, Y Fan, M Tse, KY Lin - Computers & Industrial Engineering, 2020 - Elsevier
Federated Learning (FL) is a collaboratively decentralized privacy-preserving technology to
overcome challenges of data silos and data sensibility. Exactly what research is carrying the …
overcome challenges of data silos and data sensibility. Exactly what research is carrying the …
Distributed load forecasting using smart meter data: Federated learning with Recurrent Neural Networks
Load forecasting is essential for energy management, infrastructure planning, grid
operation, and budgeting. Large scale smart meter deployments have resulted in ability to …
operation, and budgeting. Large scale smart meter deployments have resulted in ability to …
Local differential privacy-based federated learning for internet of things
The Internet of Vehicles (IoV) is a promising branch of the Internet of Things. IoV simulates a
large variety of crowdsourcing applications, such as Waze, Uber, and Amazon Mechanical …
large variety of crowdsourcing applications, such as Waze, Uber, and Amazon Mechanical …
Provably secure federated learning against malicious clients
Federated learning enables clients to collaboratively learn a shared global model without
sharing their local training data with a cloud server. However, malicious clients can corrupt …
sharing their local training data with a cloud server. However, malicious clients can corrupt …
A survey of trustworthy federated learning: Issues, solutions, and challenges
Trustworthy artificial intelligence (TAI) has proven invaluable in curbing potential negative
repercussions tied to AI applications. Within the TAI spectrum, federated learning (FL) …
repercussions tied to AI applications. Within the TAI spectrum, federated learning (FL) …
A comprehensive survey on local differential privacy toward data statistics and analysis
T Wang, X Zhang, J Feng, X Yang - Sensors, 2020 - mdpi.com
Collecting and analyzing massive data generated from smart devices have become
increasingly pervasive in crowdsensing, which are the building blocks for data-driven …
increasingly pervasive in crowdsensing, which are the building blocks for data-driven …
Hierarchical personalized federated learning for user modeling
User modeling aims to capture the latent characteristics of users from their behaviors, and is
widely applied in numerous applications. Usually, centralized user modeling suffers from the …
widely applied in numerous applications. Usually, centralized user modeling suffers from the …
Federated learning via decentralized dataset distillation in resource-constrained edge environments
In federated learning, all networked clients contribute to the model training cooperatively.
However, with model sizes increasing, even sharing the trained partial models often leads to …
However, with model sizes increasing, even sharing the trained partial models often leads to …