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Federated machine learning: Survey, multi-level classification, desirable criteria and future directions in communication and networking systems
The communication and networking field is hungry for machine learning decision-making
solutions to replace the traditional model-driven approaches that proved to be not rich …
solutions to replace the traditional model-driven approaches that proved to be not rich …
A survey on trust models in heterogeneous networks
Heterogeneous networks (HetNets) merge different types of networks into an integrated
network system, which has become a hot research area in recent years towards next …
network system, which has become a hot research area in recent years towards next …
Federated against the cold: A trust-based federated learning approach to counter the cold start problem in recommendation systems
Recommendation systems are often challenged by the existence of cold-start items for which
no previous rating is available. The standard content-based or collaborative-filtering …
no previous rating is available. The standard content-based or collaborative-filtering …
A survey on explainable artificial intelligence for cybersecurity
The “black-box” nature of artificial intelligence (AI) models has been the source of many
concerns in their use for critical applications. Explainable Artificial Intelligence (XAI) is a …
concerns in their use for critical applications. Explainable Artificial Intelligence (XAI) is a …
Deep and reinforcement learning for automated task scheduling in large‐scale cloud computing systems
Cloud computing is undeniably becoming the main computing and storage platform for
today's major workloads. From Internet of things and Industry 4.0 workloads to big data …
today's major workloads. From Internet of things and Industry 4.0 workloads to big data …
Trust-driven reinforcement selection strategy for federated learning on IoT devices
Federated learning is a distributed machine learning approach that enables a large number
of edge/end devices to perform on-device training for a single machine learning model …
of edge/end devices to perform on-device training for a single machine learning model …
Autonomous robotic manipulation: real‐time, deep‐learning approach for gras** of unknown objects
Recent advancement in vision‐based robotics and deep‐learning techniques has enabled
the use of intelligent systems in a wider range of applications requiring object manipulation …
the use of intelligent systems in a wider range of applications requiring object manipulation …
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 …
[HTML][HTML] A novel model based collaborative filtering recommender system via truncated ULV decomposition
Collaborative filtering is a technique that takes into account the common characteristics of
users and items in recommender systems. Matrix decompositions are one of the most used …
users and items in recommender systems. Matrix decompositions are one of the most used …
Explainable AI-based federated deep reinforcement learning for trusted autonomous driving
Recently, the concept of autonomous driving became prevalent in the domain of intelligent
transportation due to the promises of increased safety, traffic efficiency, fuel economy and …
transportation due to the promises of increased safety, traffic efficiency, fuel economy and …