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Found 8 papers, 1 papers with code
The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions
To address these issues, we present in this survey a novel multi-layered pipeline ecosystem composed of (1) the Metaverse computing, networking, communications and hardware infrastructure, (2) environment digitization, and (3) user interactions.
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
Explainable Artificial Intelligence (XAI)
ON-DEMAND-FL: A Dynamic and Efficient Multi-Criteria Federated Learning Client Deployment Scheme
In this paper, we address the aforementioned limitations by introducing an On-Demand-FL, a client deployment approach for FL, offering more volume and heterogeneity of data in the learning process.
ModularFed: Leveraging Modularity in Federated Learning Frameworks
Numerous research recently proposed integrating Federated Learning (FL) to address the privacy concerns of using machine learning in privacy-sensitive firms.
FedMint: Intelligent Bilateral Client Selection in Federated Learning with Newcomer IoT Devices
Our solution involves the design of: (1) preference functions for the client IoT devices and federated servers to allow them to rank each other according to several factors such as accuracy and price, (2) intelligent matching algorithms that take into account the preferences of both parties in their design, and (3) bootstrapping technique that capitalizes on the collaboration of multiple federated servers in order to assign initial accuracy value for the newly connected IoT devices.
Reward Shaping Using Convolutional Neural Network
Due to (1) the previous success of using message passing for reward shaping; and (2) the CNN planning behavior, we use these messages to train the CNN of VIN-RS.
Reinforcement Learning Framework for Server Placement and Workload Allocation in Multi-Access Edge Computing
Thus, multi-access edge computing (MEC), which consists of distributing the edge servers in the proximity of end-users to have low latency besides the higher processing power, is increasingly becoming a vital factor for the success of modern applications.
A two-level solution to fight against dishonest opinions in recommendation-based trust systems
In this paper, we propose a mechanism to deal with dishonest opinions in recommendation-based trust models, at both the collection and processing levels.
