Papers Privacy Preserving
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A Privacy-Preserving Semantic-Segmentation Method Using Domain-Adaptation Technique
We propose a privacy-preserving semantic-segmentation method for applying perceptual encryption to images used for model training in addition to test images. This method also provides almost the same accuracy as models w…
Domain AdaptationPrivacy PreservingSegmentationSemantic SegmentationFederated Learning for Commercial Image Sources
Federated Learning is a collaborative machine learning paradigm that enables multiple clients to learn a global model without exposing their data to each other. Consequently, it provides a secure learning platform with p…
Federated Learningimage-classificationImage ClassificationPrivacy PreservingTransformer-Based Person Identification via Wi-Fi CSI Amplitude and Phase Perturbations
Wi-Fi sensing is gaining momentum as a non-intrusive and privacy-preserving alternative to vision-based systems for human identification. However, person identification through wireless signals, particularly without user…
Person IdentificationPrivacy PreservingPrivacy-Preserving Fusion for Multi-Sensor Systems Under Multiple Packet Dropouts
Wireless sensor networks (WSNs) are critical components in modern cyber-physical systems, enabling efficient data collection and fusion through spatially distributed sensors. However, the inherent risks of eavesdropping …
Privacy PreservingState EstimationFederated Learning in Open- and Closed-Loop EMG Decoding: A Privacy and Performance Perspective
Invasive and non-invasive neural interfaces hold promise as high-bandwidth input devices for next-generation technologies. However, neural signals inherently encode sensitive information about an individual's identity an…
Federated LearningPrivacy PreservingSafeguarding Federated Learning-based Road Condition Classification
Federated Learning (FL) has emerged as a promising solution for privacy-preserving autonomous driving, specifically camera-based Road Condition Classification (RCC) systems, harnessing distributed sensing, computing, and…
Autonomous DrivingClassificationFederated LearningPrivacy PreservingA Privacy-Preserving Framework for Advertising Personalization Incorporating Federated Learning and Differential Privacy
To mitigate privacy leakage and performance issues in personalized advertising, this paper proposes a framework that integrates federated learning and differential privacy. The system combines distributed feature extract…
Anomaly DetectionFederated LearningPrivacy PreservingZKP-FedEval: Verifiable and Privacy-Preserving Federated Evaluation using Zero-Knowledge Proofs
Federated Learning (FL) enables collaborative model training on decentralized data without exposing raw data. However, the evaluation phase in FL may leak sensitive information through shared performance metrics. In this…
Activity RecognitionFederated LearningHuman Activity RecognitionPrivacy PreservingPrivacy-Preserving Multi-Stage Fall Detection Framework with Semi-supervised Federated Learning and Robotic Vision Confirmation
The aging population is growing rapidly, and so is the danger of falls in older adults. A major cause of injury is falling, and detection in time can greatly save medical expenses and recovery time. However, to provide t…
Federated LearningIndoor LocalizationNavigatePrivacy PreservingDomain Borders Are There to Be Crossed With Federated Few-Shot Adaptation
Federated Learning has emerged as a leading paradigm for decentralized, privacy-preserving learning, particularly relevant in the era of interconnected edge devices equipped with sensors. However, the practical implement…
Domain AdaptationFederated LearningPrivacy PreservingDifferentially Private Federated Low Rank Adaptation Beyond Fixed-Matrix
Large language models (LLMs) typically require fine-tuning for domain-specific tasks, and LoRA offers a computationally efficient approach by training low-rank adapters. LoRA is also communication-efficient for federated…
Privacy PreservingGeo-ORBIT: A Federated Digital Twin Framework for Scene-Adaptive Lane Geometry Detection
Digital Twins (DT) have the potential to transform traffic management and operations by creating dynamic, virtual representations of transportation systems that sense conditions, analyze operations, and support decision-…
Computational EfficiencyFederated LearningLane DetectionMeta-Learning+1Quantum Federated Learning for Multimodal Data: A Modality-Agnostic Approach
Quantum federated learning (QFL) has been recently introduced to enable a distributed privacy-preserving quantum machine learning (QML) model training across quantum processors (clients). Despite recent research efforts,…
Federated LearningPrivacy PreservingQuantum Machine LearningBalancing the Past and Present: A Coordinated Replay Framework for Federated Class-Incremental Learning
Federated Class Incremental Learning (FCIL) aims to collaboratively process continuously increasing incoming tasks across multiple clients. Among various approaches, data replay has become a promising solution, which can…
class-incremental learningClass Incremental LearningIncremental LearningPrivacy PreservingHLF-FSL. A Decentralized Federated Split Learning Solution for IoT on Hyperledger Fabric
Collaborative machine learning in sensitive domains demands scalable, privacy preserving solutions for enterprise deployment. Conventional Federated Learning (FL) relies on a central server, introducing single points of …
Federated LearningPrivacy PreservingSparse Self-Federated Learning for Energy Efficient Cooperative Intelligence in Society 5.0
Federated Learning offers privacy-preserving collaborative intelligence but struggles to meet the sustainability demands of emerging IoT ecosystems necessary for Society 5.0-a human-centered technological future balancin…
Federated LearningPrivacy PreservingAn Enhanced Privacy-preserving Federated Few-shot Learning Framework for Respiratory Disease Diagnosis
The labor-intensive nature of medical data annotation presents a significant challenge for respiratory disease diagnosis, resulting in a scarcity of high-quality labeled datasets in resource-constrained settings. Moreove…
DiagnosticFew-Shot LearningPrivacy PreservingTowards Privacy-Preserving and Personalized Smart Homes via Tailored Small Language Models
Large Language Models (LLMs) have showcased remarkable generalizability in language comprehension and hold significant potential to revolutionize human-computer interaction in smart homes. Existing LLM-based smart home a…
Privacy PreservingSmall Language ModelCommunication-Efficient Module-Wise Federated Learning for Grasp Pose Detection in Cluttered Environments
Grasp pose detection (GPD) is a fundamental capability for robotic autonomy, but its reliance on large, diverse datasets creates significant data privacy and centralization challenges. Federated Learning (FL) offers a pr…
Federated LearningPrivacy PreservingDESIGN: Encrypted GNN Inference via Server-Side Input Graph Pruning
Graph Neural Networks (GNNs) have achieved state-of-the-art performance in various graph-based learning tasks. However, enabling privacy-preserving GNNs in encrypted domains, such as under Fully Homomorphic Encryption (F…
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