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Found 21 papers, 1 papers with code
Federated Learning for Cyber Physical Systems: A Comprehensive Survey
Federated learning (FL), a distributed approach to ML, has become increasingly popular in recent years.
Quantum-Inspired Genetic Algorithm for Robust Source Separation in Smart City Acoustics
This paper introduces a novel Quantum-Inspired Genetic Algorithm (p-QIGA) for source separation, drawing inspiration from quantum information theory to enhance acoustic scene analysis in smart cities.
Enhancing Federated Learning Through Secure Cluster-Weighted Client Aggregation
Federated learning (FL) has emerged as a promising paradigm in machine learning, enabling collaborative model training across decentralized devices without the need for raw data sharing.
Adaptive Clipping for Privacy-Preserving Few-Shot Learning: Enhancing Generalization with Limited Data
These challenges are particularly pronounced in the domain of few-shot learning, where the ability to learn from limited labeled data is crucial.
Multi-Objective Optimization for Privacy-Utility Balance in Differentially Private Federated Learning
In this work, we propose an adaptive clipping mechanism that dynamically adjusts the clipping norm using a multi-objective optimization framework.
Federated Learning with Differential Privacy: An Utility-Enhanced Approach
In order to successfully avoid data leakage, adopting differential privacy (DP) in the local optimization process or in the local update aggregation process has emerged as two feasible ways for achieving sample-level or user-level privacy guarantees respectively, in federated learning models.
Quantum-Enhanced Transformers for Robust Acoustic Scene Classification in IoT Environments
The proliferation of Internet of Things (IoT) devices equipped with acoustic sensors necessitates robust acoustic scene classification (ASC) capabilities, even in noisy and data-limited environments.
VR Based Emotion Recognition Using Deep Multimodal Fusion With Biosignals Across Multiple Anatomical Domains
Emotion recognition is significantly enhanced by integrating multimodal biosignals and IMU data from multiple domains.
HierSFL: Local Differential Privacy-aided Split Federated Learning in Mobile Edge Computing
To tackle this problem, Split Federated Learning is utilized, where clients upload their intermediate model training outcomes to a cloud server for collaborative server-client model training.
Holistic Survey of Privacy and Fairness in Machine Learning
Privacy and fairness are two crucial pillars of responsible Artificial Intelligence (AI) and trustworthy Machine Learning (ML).
Latency Optimization for Blockchain-Empowered Federated Learning in Multi-Server Edge Computing
To assist the ML model training for resource-constrained MDs, we develop an offloading strategy that enables MDs to transmit their data to one of the associated ESs.
Federated Learning for Smart Healthcare: A Survey
Recent advances in communication technologies and Internet-of-Medical-Things have transformed smart healthcare enabled by artificial intelligence (AI).
Federated Learning for COVID-19 Detection with Generative Adversarial Networks in Edge Cloud Computing
COVID-19 has spread rapidly across the globe and become a deadly pandemic.
Cooperative Task Offloading and Block Mining in Blockchain-based Edge Computing with Multi-agent Deep Reinforcement Learning
The convergence of mobile edge computing (MEC) and blockchain is transforming the current computing services in mobile networks, by offering task offloading solutions with security enhancement empowered by blockchain mining.
BEdgeHealth: A Decentralized Architecture for Edge-based IoMT Networks Using Blockchain
The healthcare industry has witnessed significant transformations in e-health services by using mobile edge computing (MEC) and blockchain to facilitate healthcare operations.
6G Internet of Things: A Comprehensive Survey
The sixth generation (6G) wireless communication networks are envisioned to revolutionize customer services and applications via the Internet of Things (IoT) towards a future of fully intelligent and autonomous systems.
Federated Learning for Industrial Internet of Things in Future Industries
The Industrial Internet of Things (IIoT) offers promising opportunities to transform the operation of industrial systems and becomes a key enabler for future industries.
Federated Learning for Internet of Things: A Comprehensive Survey
The Internet of Things (IoT) is penetrating many facets of our daily life with the proliferation of intelligent services and applications empowered by artificial intelligence (AI).
Swarm Intelligence for Next-Generation Wireless Networks: Recent Advances and Applications
Due to the proliferation of smart devices and emerging applications, many next-generation technologies have been paid for the development of wireless networks.
Privacy-Preserved Task Offloading in Mobile Blockchain with Deep Reinforcement Learning
Blockchain technology with its secure, transparent and decentralized nature has been recently employed in many mobile applications.
Secure Computation Offloading in Blockchain based IoT Networks with Deep Reinforcement Learning
How to implement offloading to alleviate computation burdens at MDs while guaranteeing high security in mobile edge cloud is a challenging problem.
