Handling privacy-sensitive medical data with federated learning: challenges and future directions
Recent medical applications are largely dominated by the application of Machine Learning
(ML) models to assist expert decisions, leading to disruptive innovations in radiology …
(ML) models to assist expert decisions, leading to disruptive innovations in radiology …
A machine learning approach for task and resource allocation in mobile-edge computing-based networks
In this article, a joint task, spectrum, and transmit power allocation problem is investigated for
a wireless network in which the base stations (BSs) are equipped with mobile-edge …
a wireless network in which the base stations (BSs) are equipped with mobile-edge …
Enhancing federated learning in fog-aided IoT by CPU frequency and wireless power control
Machine learning models have been built in fog nodes in fog-aided Internet-of-Things (IoT)
networks to provision future events prediction and image classification by training data …
networks to provision future events prediction and image classification by training data …
Communication and computation efficiency in federated learning: A survey
Federated Learning is a much-needed technology in this golden era of big data and Artificial
Intelligence, due to its vital role in preserving data privacy, and eliminating the need to …
Intelligence, due to its vital role in preserving data privacy, and eliminating the need to …
Trust-augmented deep reinforcement learning for federated learning client selection
In the context of distributed machine learning, the concept of federated learning (FL) has
emerged as a solution to the privacy concerns that users have about sharing their own data …
emerged as a solution to the privacy concerns that users have about sharing their own data …
A review of artificial intelligence in security and privacy: Research advances, applications, opportunities, and challenges
YA Al-Khassawneh - Indonesian Journal of Science and …, 2023 - ejournal.kjpupi.id
Artificial intelligence has the potential to address many societal, economic, and
environmental challenges, but only if AI-enabled gadgets are kept secure. Many artificial …
environmental challenges, but only if AI-enabled gadgets are kept secure. Many artificial …
Federated learning over wireless networks: Challenges and solutions
M Beitollahi, N Lu - IEEE Internet of Things Journal, 2023 - ieeexplore.ieee.org
Motivated by ever-increasing computational resources at edge devices and increasing
privacy concerns, a new machine learning (ML) framework called federated learning (FL) …
privacy concerns, a new machine learning (ML) framework called federated learning (FL) …
Resource allocation in mobility-aware federated learning networks: A deep reinforcement learning approach
Federated learning allows mobile devices, ie, workers, to use their local data to
collaboratively train a global model required by the model owner. Federated learning thus …
collaboratively train a global model required by the model owner. Federated learning thus …
Energy-efficient federated learning with resource allocation for green IoT edge intelligence in B5G
An edge intelligence-aided Internet-of-Things (IoT) network has been proposed to
accelerate the response of IoT services by deploying edge intelligence near IoT devices …
accelerate the response of IoT services by deploying edge intelligence near IoT devices …
Adaptive deadline determination for mobile device selection in federated learning
Owing to dynamically changing resources and channel conditions of mobile devices (MDs),
when a static deadline-based MD selection scheme is used for federated learning, resource …
when a static deadline-based MD selection scheme is used for federated learning, resource …