Federated learning for internet of things: A comprehensive survey
The Internet of Things (IoT) is penetrating many facets of our daily life with the proliferation of
intelligent services and applications empowered by artificial intelligence (AI). Traditionally …
intelligent services and applications empowered by artificial intelligence (AI). Traditionally …
Federated learning for smart healthcare: A survey
Recent advances in communication technologies and the Internet-of-Medical-Things (IOMT)
have transformed smart healthcare enabled by artificial intelligence (AI). Traditionally, AI …
have transformed smart healthcare enabled by artificial intelligence (AI). Traditionally, AI …
Advances and open problems in federated learning
Federated learning (FL) is a machine learning setting where many clients (eg, mobile
devices or whole organizations) collaboratively train a model under the orchestration of a …
devices or whole organizations) collaboratively train a model under the orchestration of a …
Federated machine learning: Concept and applications
Today's artificial intelligence still faces two major challenges. One is that, in most industries,
data exists in the form of isolated islands. The other is the strengthening of data privacy and …
data exists in the form of isolated islands. The other is the strengthening of data privacy and …
A survey on security and privacy of federated learning
Federated learning (FL) is a new breed of Artificial Intelligence (AI) that builds upon
decentralized data and training that brings learning to the edge or directly on-device. FL is a …
decentralized data and training that brings learning to the edge or directly on-device. FL is a …
Deep leakage from gradients
Passing gradient is a widely used scheme in modern multi-node learning system (eg,
distributed training, collaborative learning). In a long time, people used to believe that …
distributed training, collaborative learning). In a long time, people used to believe that …
Communication-efficient learning of deep networks from decentralized data
Modern mobile devices have access to a wealth of data suitable for learning models, which
in turn can greatly improve the user experience on the device. For example, language …
in turn can greatly improve the user experience on the device. For example, language …
Federated learning for mobile keyboard prediction
We train a recurrent neural network language model using a distributed, on-device learning
framework called federated learning for the purpose of next-word prediction in a virtual …
framework called federated learning for the purpose of next-word prediction in a virtual …
Adaptive federated learning in resource constrained edge computing systems
Emerging technologies and applications including Internet of Things, social networking, and
crowd-sourcing generate large amounts of data at the network edge. Machine learning …
crowd-sourcing generate large amounts of data at the network edge. Machine learning …
A survey on federated learning: The journey from centralized to distributed on-site learning and beyond
S AbdulRahman, H Tout… - IEEE Internet of …, 2020 - ieeexplore.ieee.org
Driven by privacy concerns and the visions of deep learning, the last four years have
witnessed a paradigm shift in the applicability mechanism of machine learning (ML). An …
witnessed a paradigm shift in the applicability mechanism of machine learning (ML). An …