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Found 14 papers, 1 papers with code
Trust Driven On-Demand Scheme for Client Deployment in Federated Learning
In our research, we are motivated to integrate a trust element into the client selection and model deployment processes within our system architecture.
Enhancing Mutual Trustworthiness in Federated Learning for Data-Rich Smart Cities
Federated learning is a promising collaborative and privacy-preserving machine learning approach in data-rich smart cities.
Perception, performance, and detectability of conversational artificial intelligence across 32 university courses
Moreover, current AI-text classifiers cannot reliably detect ChatGPT's use in school work, due to their propensity to classify human-written answers as AI-generated, as well as the ease with which AI-generated text can be edited to evade detection.
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)
Warmup and Transfer Knowledge-Based Federated Learning Approach for IoT Continuous Authentication
Most of the literature focuses on training machine learning for the user by transmitting their data to an external server, subject to private user data exposure to threats.
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.
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.
A Federated Learning Scheme for Neuro-developmental Disorders: Multi-Aspect ASD Detection
Autism Spectrum Disorder (ASD) is a neuro-developmental syndrome resulting from alterations in the embryological brain before birth.
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.
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.
Critical Impact of Social Networks Infodemic on Defeating Coronavirus COVID-19 Pandemic: Twitter-Based Study and Research Directions
The widespread of the Coronavirus COVID-19 resulted with a tsunami of social media.
