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 …
Federated learning for smart cities: A comprehensive survey
With the advent of new technologies such as the Artificial Intelligence of Things (AIoT), big
data, fog computing, and edge computing, smart city applications have suffered from issues …
data, fog computing, and edge computing, smart city applications have suffered from issues …
Heterogeneous computation and resource allocation for wireless powered federated edge learning systems
Federated learning (FL) is a popular edge learning approach that utilizes local data and
computing resources of network edge devices to train machine learning (ML) models while …
computing resources of network edge devices to train machine learning (ML) models while …
DetFed: Dynamic resource scheduling for deterministic federated learning over time-sensitive networks
In this paper, we present a three-layer (ie, device, field, and factory layers) deterministic
federated learning (FL) framework, named DetFed, which accelerates collaborative learning …
federated learning (FL) framework, named DetFed, which accelerates collaborative learning …
Ai-based mobile edge computing for iot: Applications, challenges, and future scope
New technology is needed to meet the latency and bandwidth issues present in cloud
computing architecture specially to support the currency of 5G networks. Accordingly, mobile …
computing architecture specially to support the currency of 5G networks. Accordingly, mobile …
Adoption of federated learning for healthcare informatics: Emerging applications and future directions
The smart healthcare system has improved the patients quality of life (QoL), where the
records are being analyzed remotely by distributed stakeholders. It requires a voluminous …
records are being analyzed remotely by distributed stakeholders. It requires a voluminous …
HiFlash: Communication-efficient hierarchical federated learning with adaptive staleness control and heterogeneity-aware client-edge association
Federated learning (FL) is a promising paradigm that enables collaboratively learning a
shared model across massive clients while keeping the training data locally. However, for …
shared model across massive clients while keeping the training data locally. However, for …
On-board federated learning for dense LEO constellations
Mega-constellations of small-size Low Earth Orbit (LEO) satellites are currently planned and
deployed by various private and public entities. While global connectivity is the main …
deployed by various private and public entities. While global connectivity is the main …
Bandwidth allocation for multiple federated learning services in wireless edge networks
This paper studies a federated learning (FL) system, where multiple FL services co-exist in a
wireless network and share common wireless resources. It fills the void of wireless resource …
wireless network and share common wireless resources. It fills the void of wireless resource …
Cheese: Distributed clustering-based hybrid federated split learning over edge networks
Implementing either Federated learning (FL) or split learning (SL) over clients with limited
computation/communication resources faces challenges on achieving delay-efficient model …
computation/communication resources faces challenges on achieving delay-efficient model …