The stringent requirements for low-latency and privacy of the emerging high-stake applications with intelligent devices such as drones and smart vehicles make the cloud …
Recent years have witnessed a rapid proliferation of smart Internet of Things (IoT) devices. IoT devices with intelligence require the use of effective machine learning paradigms …
With its ability to provide a single platform enabling a variety of services, such as enhanced mobile broadband communications, virtual reality, automated driving, and the Internet of …
In recent times, we assist to an ever growing diffusion of smart medical sensors and Internet of things devices that are heavily changing the way healthcare is approached worldwide. In …
Y Jiang, S Wang, V Valls, BJ Ko… - … on Neural Networks …, 2022 - ieeexplore.ieee.org
Federated learning (FL) allows model training from local data collected by edge/mobile devices while preserving data privacy, which has wide applicability to image and vision …
Emerging technologies and applications including Internet of Things, social networking, and crowd-sourcing generate large amounts of data at the network edge. Machine learning …
H Kim, J Park, M Bennis, SL Kim - IEEE Communications …, 2019 - ieeexplore.ieee.org
By leveraging blockchain, this letter proposes a blockchained federated learning (BlockFL) architecture where local learning model updates are exchanged and verified. This enables …
Artificial intelligence (AI) has achieved remarkable breakthroughs in a wide range of fields, ranging from speech processing, image classification to drug discovery. This is driven by the …
MM Amiri, D Gündüz - IEEE Transactions on Signal Processing, 2020 - ieeexplore.ieee.org
We study federated machine learning (ML) at the wireless edge, where power-and bandwidth-limited wireless devices with local datasets carry out distributed stochastic …