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• Lightweight Federated Learning Frameworks for Privacy-Preserving Predictive Analytics in Remote Patient Monitoring: A Systematic Review
  Remote patient monitoring (RPM) via Internet of Healthcare Things (IoHT) devices creates a tension between the clinical value of continuous physiological data and the imperative to protect patient privacy. Federated learning (FL) resolves this by enabling coll…
  datacite D. Sucharitha, Dr. M. Senthil Kumaran 2026 置信度 0.66 Federated Learning; Remote Patient Monitoring; IoHT; Lightweight Edge AI; Differential Privacy; Privacy–Accuracy Trade-off; PRISMA; Systematic Review
• Lightweight Federated Learning Frameworks for Privacy-Preserving Predictive Analytics in Remote Patient Monitoring: A Systematic Review
  Remote patient monitoring (RPM) via Internet of Healthcare Things (IoHT) devices creates a tension between the clinical value of continuous physiological data and the imperative to protect patient privacy. Federated learning (FL) resolves this by enabling coll…
  datacite D. Sucharitha, Dr. M. Senthil Kumaran 2026 置信度 0.66 Federated Learning; Remote Patient Monitoring; IoHT; Lightweight Edge AI; Differential Privacy; Privacy–Accuracy Trade-off; PRISMA; Systematic Review
• QuantumChain: Blockchain-Backed Quantum Federated Learning for Financial Fraud Detection
  Financial fraud detection is challenged by decentralized data, severe class imbalance, and privacy constraints. This paper presents QuantumChain, a secure Quantum Federated Learning (QFL) framework that combines hybrid quantum-classical neural networks, encryp…
  datacite Douros, Epameinondas, Dalampekis, Konstantinos, Innan, Nouhaila, Theodonis, Ioannis 等 2026 置信度 0.66 Quantum Physics (quant-ph)FOS: Physical sciences
• Pre-Deployment Complexity Estimation for Federated Perception Systems
  Edge AI systems increasingly rely on federated learning to train perception models in distributed, privacy-preserving, and resource-constrained environments. Before training, however, practitioners often lack practical tools for estimating task difficulty in t…
  datacite Solaiman, KMA, Islam, Shafkat, de Oliveira, Ruy, Bhargava, Bharat 2026 置信度 0.66 Machine Learning (cs.LG)Artificial Intelligence (cs.AI)Distributed, Parallel, and Cluster Computing (cs.DC)FOS: Computer and information sciences
• SUM: Unified Geometric Surgery on Spatio-Temporal Adaptation Vectors for Federated Class Incremental Learning
  Real-world intelligent systems often require both distributed collaboration across data-isolated clients and continual adaptation to evolving tasks. This setting naturally gives rise to Federated Class Incremental Learning (FCIL), which combines Federated Lear…
  datacite Kim, Jaeik, Do, Jaeyoung 2026 置信度 0.66 Machine Learning (cs.LG)FOS: Computer and information sciences
• CO-EVO: Co-evolving Semantic Anchoring and Style Diversification for Federated DG-ReID
  Federated domain generalization for person re-identification (FedDG-ReID) aims to collaboratively train a pedestrian retrieval model across multiple decentralized source domains such that it can generalize to unseen target environments without compromising raw…
  datacite Association for Computational Linguistics 2026, ., Fengchunzhang, Hu, Jianwei, Huang, Tingxuan 等 2026 置信度 0.66 Computational LinguisticsArtificial IntelligenceInformation and Knowledge EngineeringNatural Language Processing
• Onboarding Without Forgetting: Hypernetwork Personalization with Data-Free Replay for Personalized Federated Learning
  Federated Learning (FL) enables collaborative training across distributed clients without sharing raw data, offering strong privacy benefits. However, most methods assume all clients remain available throughout training, which is unrealistic as new clients oft…
  datacite Nguyen, Thinh, Khiem, Le Huy, Tran, Van-Tuan, Doan, Khoa D 等 2025 置信度 0.66 Machine Learning (cs.LG)FOS: Computer and information sciencesC.2.4; I.2.11
• FedS2R: One-Shot Federated Domain Generalization for Synthetic-to-Real Semantic Segmentation in Autonomous Driving
  Federated domain generalization has shown promising progress in image classification by enabling collaborative training across multiple clients without sharing raw data. However, its potential in the semantic segmentation of autonomous driving remains underexp…
  datacite Lian, Tao, Gómez, Jose L., López, Antonio M. 2025 置信度 0.66 Computer Vision and Pattern Recognition (cs.CV)Artificial Intelligence (cs.AI)FOS: Computer and information sciences
• FedCod: An Efficient Communication Protocol for Cross-Silo Federated Learning with Coding
  Federated Learning (FL) is an innovative distributed machine learning paradigm that enables multiple parties to collaboratively train a model without sharing their raw data, thereby preserving data privacy. Communication efficiency concerns arise in cross-silo…
  datacite Yan, Peishen, Li, Jun, Wang, Hao, Song, Tao 等 2025 置信度 0.66 Distributed, Parallel, and Cluster Computing (cs.DC)FOS: Computer and information sciences
• AutoEncoder-Compressed Parallel Split Learning for Pre-trained Model Fine-Tuning
  Distributed Fine-Tuning (DFT) of large-scale Foundation Models (FMs) on resource-constrained edge devices is limited by local compute constraints and communication overhead. Parallel Split Learning (PSL) reduces client-side computation by keeping few model lay…
  datacite Meuwissen, Bas, Tsouvalas, Vasileios, Meratnia, Nirvana 2026 置信度 0.66 Distributed, Parallel, and Cluster Computing (cs.DC)Machine Learning (cs.LG)FOS: Computer and information sciences
• Semantic Oncology: A Knowledge Engineering Framework for Decoding Tumor Gene Networks using OWL 2 EL and Hybrid SWRL Reasoning
  English: This foundational research paper, authored by Luigi Usai (Quartucciu, Italy, April 15, 2026), introduces the "Semantic Oncology" framework—a cutting-edge paradigm in computational medicine that bridges the gap between raw genomic Big Data and actionab…
  datacite Usai, Luigi 2026 置信度 0.66
• Semantic Oncology: A Knowledge Engineering Framework for Decoding Tumor Gene Networks using OWL 2 EL and Hybrid SWRL Reasoning
  English: This foundational research paper, authored by Luigi Usai (Quartucciu, Italy, April 15, 2026), introduces the "Semantic Oncology" framework—a cutting-edge paradigm in computational medicine that bridges the gap between raw genomic Big Data and actionab…
  datacite Usai, Luigi 2026 置信度 0.66
• ARTIFICIAL INTELLIGENCE IN ADVANCED METHOD PHARMACEUTICAL ANALYSIS: FROM MACHINE LEARNING TO INTELLIGENT DRUG QUALITY ASSESSMENT
  Background: Pharmaceutical analytical science is experiencing a transformative shift with the integration of artificial intelligence (AI) and machine learning (ML) into advanced analytical platforms. Conventional analytical methods often require extensive manu…
  datacite Amartya Mitra1, Dr. Ragni Kumari2* 2026 置信度 0.66
• ARTIFICIAL INTELLIGENCE IN ADVANCED METHOD PHARMACEUTICAL ANALYSIS: FROM MACHINE LEARNING TO INTELLIGENT DRUG QUALITY ASSESSMENT
  Background: Pharmaceutical analytical science is experiencing a transformative shift with the integration of artificial intelligence (AI) and machine learning (ML) into advanced analytical platforms. Conventional analytical methods often require extensive manu…
  datacite Amartya Mitra1, Dr. Ragni Kumari2* 2026 置信度 0.66
• Stitch the Fragments: One-Shot Hierarchical Federated Clustering
  Federated Clustering (FC) faces a critical bottleneck in real-world scenarios, i.e., global clusters are rarely intact, often fragmenting into incomplete, multi-granular unlabeled ``clusterlets'' distributed across Non-IID clients. Although hierarchical cluste…
  datacite Cai, Shenghong, Yang, Zihua, Lu, Yang, Li, Mengke 等 2026 置信度 0.66 Machine Learning (cs.LG)FOS: Computer and information sciences
• FDA-Opt: Federated Fine-Tuning via Dynamic Update Schedules
  Federated Learning (FL) enables the utilization of vast, previously inaccessible data sources. At the same time, pre-trained Language Models (LMs) have taken the world by storm and for good reason. They exhibit remarkable emergent abilities and are readily ada…
  datacite Theologitis, Michael, Samoladas, Vasilis, Deligiannakis, Antonios 2025 置信度 0.66 Machine Learning (cs.LG)Distributed, Parallel, and Cluster Computing (cs.DC)FOS: Computer and information sciences
• Global Federated Learning Strategies for Building Efficient Personalized Models
  Federated learning (FL) is a practical framework that can train models on distributed user data while guaranteeing data privacy; however, due to heterogeneity in which each user has a different data distribution, problems frequently arise where both global and…
  datacite Kim, Seongyoon 2026 置信度 0.66 Machine Learning (cs.LG)FOS: Computer and information sciences
• STAR-FL: Secure Federated Learning with Spatial-Temporal Analysis and Robust Aggregation
  Data poisoning attacks pose serious security threats to Federated Learning (FL) systems in Computer Vision. Despite growing research attention, two key challenges remain for existing defense techniques: (1) accurately distinguishing between benign and maliciou…
  datacite Tabassum, Nawrin, Wu, Yanzhao 2026 置信度 0.66 Cryptography and Security (cs.CR)Machine Learning (cs.LG)FOS: Computer and information sciences
• EndoDecay-Sim: A Hybrid In-Silico Digital Twin Platform for Cardiorenal Multimorbidity Tracking with Homomorphic Encryption
  🌐 LIVE INTERACTIVE DASHBOARD PLATFORM: Researchers and peer-reviewers can access and stress-test the live, cloud-deployed clinical decision support interface directly via: https://avd9sajwiyrcgrrmunesjx.streamlit.app/ (Complete local deployment steps, CLI com…
  datacite Zavrak, Muhammet Yagiz 2026 置信度 0.66
• DisAgg: Distributed Aggregators for Efficient Secure Aggregation in Federated Learning
  Artifact archive for "DisAgg: Distributed Aggregators for Efficient Secure Aggregation in Federated Learning", MLSys 2026.
  datacite Mehmood, Haaris, Tatsis, Giorgos, Alexopoulos, Dimitrios, Saravanan, Karthikeyan 等 2026 置信度 0.66
• DisAgg: Distributed Aggregators for Efficient Secure Aggregation in Federated Learning
  Artifact archive for "DisAgg: Distributed Aggregators for Efficient Secure Aggregation in Federated Learning", MLSys 2026.
  datacite Mehmood, Haaris, Tatsis, Giorgos, Alexopoulos, Dimitrios, Saravanan, Karthikeyan 等 2026 置信度 0.66
• Innovations RT GPU et Rendu Différentiable Multi-physique
  Résumé FRCe document, produit avec l’assistance de ChatGPT 5.2 Thinking et Gemini 3 Raisonnement, est publié sous licence Apache 2.0. Il constitue une publication défensive (antériorité) et entre dans l’état de la technique dès sa mise en ligne, au sens des te…
  datacite Pillet, Xavier 2026 置信度 0.66 G06T 15/50G06F 9/50G06N 20/00G06T 15/20H04B 7/26
• Innovations RT GPU et Rendu Différentiable Multi-physique
  Résumé FRCe document, produit avec l’assistance de ChatGPT 5.2 Thinking et Gemini 3 Raisonnement, est publié sous licence Apache 2.0. Il constitue une publication défensive (antériorité) et entre dans l’état de la technique dès sa mise en ligne, au sens des te…
  datacite Pillet, Xavier 2026 置信度 0.66 G06T 15/50G06F 9/50G06N 20/00G06T 15/20H04B 7/26
• The Metabolic Age Institutional Playbook
  The Metabolic Age Institutional Playbook I. Purpose of the Playbook The global transition from industrial computation to sovereign, metabolic infrastructure represents a fundamental paradigm shift in the governance of intelligence, resources, and institutional…
  datacite Brewer, Mark Anthony 2026 置信度 0.66
• The Metabolic Age Institutional Playbook
  The Metabolic Age Institutional Playbook I. Purpose of the Playbook The global transition from industrial computation to sovereign, metabolic infrastructure represents a fundamental paradigm shift in the governance of intelligence, resources, and institutional…
  datacite Brewer, Mark Anthony 2026 置信度 0.66
• Robotique souple neuromorphique et essaims
  Résumé FRCe document, produit avec l’assistance de ChatGPT 5.2 Thinking et Gemini 3 Raisonnement, est publié sous licence Apache 2.0. Il constitue une publication défensive (antériorité) et entre dans l’état de la technique au sens des textes applicables (EPC …
  datacite Pillet, Xavier 2026 置信度 0.66 B25J 15/00B25J 15/12G01D 18/00G01L 5/00G01L 5/16
• Robotique souple neuromorphique et essaims
  Résumé FRCe document, produit avec l’assistance de ChatGPT 5.2 Thinking et Gemini 3 Raisonnement, est publié sous licence Apache 2.0. Il constitue une publication défensive (antériorité) et entre dans l’état de la technique au sens des textes applicables (EPC …
  datacite Pillet, Xavier 2026 置信度 0.66 B25J 15/00B25J 15/12G01D 18/00G01L 5/00G01L 5/16
• Analysis code and derived dataset for: Federated learning reconciles data sovereignty with collaborative energy-transition modelling across the Global South
  Analysis code, derived dataset and outputs for the manuscript ‘Federated learning reconciles data sovereignty with collaborative energy-transition modelling across the Global South: a public-data benchmark anchored in Indonesia’ (submitted to npj Climate Actio…
  datacite Darmanto, Sumartono 2026 置信度 0.66 federated learning; energy transition; data sovereignty; Global South; Indonesia; renewable energy
• Could Model Partitioning Make Federated Learning More Sustainable?
  As federated learning (FL) extends from distributed machine learning between low-power devices to cross-silo scenarios involving edge servers and data centres, its carbon footprint has become a growing concern. Addressing this, methods for sustainable FL align…
  datacite Frohlich, Tobias, Vlaar, Tiffany, Thamsen, Lauritz 2026 置信度 0.66 Distributed, Parallel, and Cluster Computing (cs.DC)FOS: Computer and information sciences
• Les Enjeux de la Rencontre Amoureuse dans les Sociétés Contemporaines
  Résum�� FRCe document, produit avec l’assistance de Gemini 3 Raisonnement, est publié sous licence Apache 2.0. Il constitue une publication défensive volontaire (antériorité) et entre de ce fait dans l’état de la technique au sens des législations applicables …
  datacite Pillet, Xavier 2026 置信度 0.66 A61M 21/00B60R 21/013C12Q 1/6886E04B 1/74E04B 1/76
• Les Enjeux de la Rencontre Amoureuse dans les Sociétés Contemporaines
  Résumé FRCe document, produit avec l’assistance de Gemini 3 Raisonnement, est publié sous licence Apache 2.0. Il constitue une publication défensive volontaire (antériorité) et entre de ce fait dans l’état de la technique au sens des législations applicables (…
  datacite Pillet, Xavier 2026 置信度 0.66 A61M 21/00B60R 21/013C12Q 1/6886E04B 1/74E04B 1/76
• Étude de faisabilité et analyse de marché – Innovations pour réacteurs à fusion compacts (ARC, SPARC, ST-E1)
  Résumé FRCe document, produit avec l’assistance de ChatGPT 5.2 Thinking et Gemini 3 Raisonnement, est publié sous licence Apache 2.0. Il constitue une publication défensive (antériorité) et entre donc dans l’état de la technique au sens des textes applicables …
  datacite Pillet, Xavier 2026 置信度 0.66 G21B 1/00G21B 1/04Y02E 30/30C09D 5/00B22F 3/105
• Étude de faisabilité et analyse de marché – Innovations pour réacteurs à fusion compacts (ARC, SPARC, ST-E1)
  Résumé FRCe document, produit avec l’assistance de ChatGPT 5.2 Thinking et Gemini 3 Raisonnement, est publié sous licence Apache 2.0. Il constitue une publication défensive (antériorité) et entre donc dans l’état de la technique au sens des textes applicables …
  datacite Pillet, Xavier 2026 置信度 0.66 G21B 1/00G21B 1/04Y02E 30/30C09D 5/00B22F 3/105
• Étude de faisabilité et analyse de marché – Innovations pour réacteurs à fusion compacts (ARC, SPARC, ST-E1)
  Résumé FRCe document, produit avec l’assistance de ChatGPT 5.2 Thinking et Gemini 3 Raisonnement, est publié sous licence Apache 2.0. Il constitue une publication défensive (antériorité) et entre donc dans l’état de la technique au sens des textes applicables …
  datacite Pillet, Xavier 2026 置信度 0.66 G21B 1/00G21B 1/04Y02E 30/30C09D 5/00B22F 3/105
• Étude de faisabilité et analyse de marché – Innovations pour réacteurs à fusion compacts (ARC, SPARC, ST-E1)
  Résumé FRCe document, produit avec l’assistance de ChatGPT 5.2 Thinking et Gemini 3 Raisonnement, est publié sous licence Apache 2.0. Il constitue une publication défensive (antériorité) et entre donc dans l’état de la technique au sens des textes applicables …
  datacite Pillet, Xavier 2026 置信度 0.66 G21B 1/00G21B 1/04Y02E 30/30C09D 5/00B22F 3/105
• Cloud Computing Security in the Age of AI: A Systematic Review Using Artificial Intelligence as Both Research Instrument and Object of Study
  The rapid migration of organizational infrastructure to cloud computing has introduced unprecedented scalability and efficiency alongside increasingly complex security vulnerabilities. This review examines the evolving relationship between artificial intellige…
  datacite Uwaisu Abubakar Umar, Ibrahim Haruna Ibrahim, Aliyu Aminu Dahiru, Emmanuel Martin Teman 2026 置信度 0.66
• Cloud Computing Security in the Age of AI: A Systematic Review Using Artificial Intelligence as Both Research Instrument and Object of Study
  The rapid migration of organizational infrastructure to cloud computing has introduced unprecedented scalability and efficiency alongside increasingly complex security vulnerabilities. This review examines the evolving relationship between artificial intellige…
  datacite Uwaisu Abubakar Umar, Ibrahim Haruna Ibrahim, Aliyu Aminu Dahiru, Emmanuel Martin Teman 2026 置信度 0.66
• Cyber Security Threat Detection using Machine Learning Techniques
  This paper provides a comprehensive analysis of machine learning in cyber security threat detection, tracing the history of its development from traditional signature-based systems towards intelligent and adaptive systems that can identify new and sophisticate…
  datacite Shah Md. Tanzimul Kabir, Md. Saiduzzaman 2026 置信度 0.66
• Cyber Security Threat Detection using Machine Learning Techniques
  This paper provides a comprehensive analysis of machine learning in cyber security threat detection, tracing the history of its development from traditional signature-based systems towards intelligent and adaptive systems that can identify new and sophisticate…
  datacite Shah Md. Tanzimul Kabir, Md. Saiduzzaman 2026 置信度 0.66
• Explainable Federated Learning for Disease Detection in Computational Pathology (JANUARY 2026)
  Traditional computational pathology systems primarily rely on centralized deep learning approaches, requiringdata from multiple hospitals to be aggregated into a singlerepository; however, such methods are impractical in realworld healthcare due to strict priv…
  datacite ABHIJITH VIJAYAN, ANAN MUHAMMED P P, POOJA P P, PROF. AJUMOL P A, PROF. JEENA JOY 2026 置信度 0.66
• Explainable Federated Learning for Disease Detection in Computational Pathology (JANUARY 2026)
  Traditional computational pathology systems primarily rely on centralized deep learning approaches, requiringdata from multiple hospitals to be aggregated into a singlerepository; however, such methods are impractical in realworld healthcare due to strict priv…
  datacite ABHIJITH VIJAYAN, ANAN MUHAMMED P P, POOJA P P, PROF. AJUMOL P A, PROF. JEENA JOY 2026 置信度 0.66
• Edge AI: A Survey of Next-Generation Waste Classification and Routing Architectures
  The recent advances in edge computing and computer vision are completely transforming the way cities handle solid waste. In this review, close attention will be paid to the way smart city systems have changed to separate various types of waste and direct users…
  datacite Yash Saini, Anmol Tyagi, Priynshu Kumar, Sagar 等 2026 置信度 0.66 Waste SegmentationEdge computingTensorflow. jsNearest Bin RecommendationSmart City Architecture
• Edge AI: A Survey of Next-Generation Waste Classification and Routing Architectures
  The recent advances in edge computing and computer vision are completely transforming the way cities handle solid waste. In this review, close attention will be paid to the way smart city systems have changed to separate various types of waste and direct users…
  datacite Yash Saini, Anmol Tyagi, Priyanshu Kumar, Sagar 等 2026 置信度 0.66 Waste SegmentationEdge computingTensorflow. jsNearest Bin RecommendationSmart City Architecture
• Edge AI: A Survey of Next-Generation Waste Classification and Routing Architectures
  The recent advances in edge computing and computer vision are completely transforming the way cities handle solid waste. In this review, close attention will be paid to the way smart city systems have changed to separate various types of waste and direct users…
  datacite Yash Saini, Anmol Tyagi, Priyanshu Kumar, Sagar 等 2026 置信度 0.66 Waste SegmentationEdge computingTensorflow. jsNearest Bin RecommendationSmart City Architecture
• Edge AI: A Survey of Next-Generation Waste Classification a nd R outing Architectures
  The recent advances in edge computing and computer vision are completely transforming the way cities handle solid waste. In this review, close attention will be paid to the way smart city systems have changed to separate various types of waste and direct users…
  datacite Yash Saini, Anmol Tyagi, Priynshu Kumar, Sagar 等 2026 置信度 0.66 Waste SegmentationEdge computingTensorflow. jsNearest Bin RecommendationSmart City Architecture
• Artificial Intelligence for Drug Repurposing and Target Discovery in Oncology
  Cancer remains a leading cause of morbidity and mortality globally, with conventional drug discovery facing significant challenges including high development costs, prolonged timelines, and substantial failure rates. Artificial intelligence (AI), encompassing …
  datacite Jayanthi Kanaka Ram*1, Arvind Nair2 2026 置信度 0.66 artificial intelligence, machine learning, deep learning, drug repurposing, target discovery, oncology, drug–target interaction, precision medicine, biomarker discovery, graph neural networks
• Artificial Intelligence for Drug Repurposing and Target Discovery in Oncology
  Cancer remains a leading cause of morbidity and mortality globally, with conventional drug discovery facing significant challenges including high development costs, prolonged timelines, and substantial failure rates. Artificial intelligence (AI), encompassing …
  datacite Jayanthi Kanaka Ram*1, Arvind Nair2 2026 置信度 0.66 artificial intelligence, machine learning, deep learning, drug repurposing, target discovery, oncology, drug–target interaction, precision medicine, biomarker discovery, graph neural networks
• T3-CIDERS: Train-the-trainer for CI Upskilling in Cybersecurity Disciplines: Insights from Fostering the First-Year Cohort
  We present the T3-CIDERS project, a “Train-the-Trainer” (T3) initiative to foster a community of practice in cyberinfrastructure (CI)- and data-enabled cybersecurity research and education. Building on the NSF-funded DeapSECURE project, T3-CIDERS prepares Futu…
  datacite Sosonkina, Masha, Wu, Hongyi, JIANG, PENG, Purwanto, Wirawan 等 2025 置信度 0.66 CybersecurityHigh-performance computingEducation
• T3-CIDERS: Train-the-trainer for CI Upskilling in Cybersecurity Disciplines: Insights from Fostering the First-Year Cohort
  We present the T3-CIDERS project, a “Train-the-Trainer” (T3) initiative to foster a community of practice in cyberinfrastructure (CI)- and data-enabled cybersecurity research and education. Building on the NSF-funded DeapSECURE project, T3-CIDERS prepares Futu…
  datacite Sosonkina, Masha, Wu, Hongyi, JIANG, PENG, Purwanto, Wirawan 等 2025 置信度 0.66 CybersecurityHigh-performance computingEducation
• The Temporal Governance Architecture: A Reference Model for Autonomous Multi Agent Systems
  This report introduces the Temporal Governance Architecture, a conceptual reference model for autonomous multi‑agent systems operating across cloud, edge, local compute, federated networks, and hybrid environments. The architecture formalizes governance‑layer …
  datacite chang, dario 2026 置信度 0.66 eu-ai-actgovernace-primitiveTelemetrydario-changGovernance
• The Temporal Governance Architecture: A Reference Model for Autonomous Multi Agent Systems
  This report introduces the Temporal Governance Architecture, a conceptual reference model for autonomous multi‑agent systems operating across cloud, edge, local compute, federated networks, and hybrid environments. The architecture formalizes governance‑layer …
  datacite chang, dario 2026 置信度 0.66 eu-ai-actgovernace-primitiveTelemetrydario-changGovernance
• CDSA-BB3: Pilot Biometric Pillar of the CDSA Federated Reinforcement Learning Paradigm — Synthetic Biometric Data, Anonymization, Cryptographic Multi-Signature, and Scaffolded FRL Extension
  CDSA-BB3 is the reference implementation of the pilot biometric pillar of the Complementary Diagnostic Safety Approach (CDSA), a third-generation aviation safety paradigm formalised as a Federated Reinforcement Learning (FRL) framework (Scenario C decision, 21…
  datacite Cantekin, Mete 2026 置信度 0.66 aviation safetyflight data recorderblack boxwearable biometricssynthetic data
• CDSA-BB3: Pilot Biometric Pillar of the CDSA Federated Reinforcement Learning Paradigm — Synthetic Biometric Data, Anonymization, Cryptographic Multi-Signature, and Scaffolded FRL Extension
  CDSA-BB3 is the reference implementation of the pilot biometric pillar of the Complementary Diagnostic Safety Approach (CDSA), a third-generation aviation safety paradigm formalised as a Federated Reinforcement Learning (FRL) framework (Scenario C decision, 21…
  datacite Cantekin, Mete 2026 置信度 0.66 aviation safetyflight data recorderblack boxwearable biometricssynthetic data
• 7th International Conference on Big Data (CBDA 2026)
  7th International Conference on Big Data (CBDA 2026) May 16 ~ 17, 2026, Zurich, Switzerland https://dma2026.org/cbda/indexScope & Topics 7th International Conference on Big Data (CBDA 2026) will provide an excellent international forum for sharing cutting edge…
  datacite Yaacoub, Aya 2026 置信度 0.66
• 7th International Conference on Big Data (CBDA 2026)
  7th International Conference on Big Data (CBDA 2026) May 16 ~ 17, 2026, Zurich, Switzerland https://dma2026.org/cbda/indexScope & Topics 7th International Conference on Big Data (CBDA 2026) will provide an excellent international forum for sharing cutting edge…
  datacite Yaacoub, Aya 2026 置信度 0.66
• EDGE AI IN 2026: A DEEP DIVE INTO SOURCE LEVEL INTELLIGENCE AND ITS FUTURE PATHWAYS
  Edge AI is one of the key paradigm shifts in the implementation of artificial intelligence, moving the inference calculation out of centralized infrastructure in the cloud and the bottom of the network, closer to the source of the data. This paper provides an …
  datacite International Journal of Technovation and Business Insights 2026 置信度 0.66 Edge AIEdge ComputingMachine Learning DeploymentFederated LearningLatency Optimization
• EDGE AI IN 2026: A DEEP DIVE INTO SOURCE LEVEL INTELLIGENCE AND ITS FUTURE PATHWAYS
  Edge AI is one of the key paradigm shifts in the implementation of artificial intelligence, moving the inference calculation out of centralized infrastructure in the cloud and the bottom of the network, closer to the source of the data. This paper provides an …
  datacite International Journal of Technovation and Business Insights 2026 置信度 0.66 Edge AIEdge ComputingMachine Learning DeploymentFederated LearningLatency Optimization
• Communication-Efficient Vehicle Sensing with Federated Learning Based on Critical Learning Period and Weight Clustering
  crossref Ping Liu, Junsheng Mu 2026-06-17T19:37:14Z 置信度 0.70
• Optimizing Global Federated Learning: A Serverless Hierarchical Approach with Region-Aware Placement
  crossref Matheus Pereira, Lúcia Drummond 2026-05-22T23:47:05Z 置信度 0.70
• Privacy-Preserved Learning in Health AI Through Federated Learning
  <div> Healthcare systems in resource limited settings face persistent challenges, including shortages of skilled professionals, limited access to diagnostic services, and fragmented healthcare infrastructure. Artificial intelligence (AI) has emerged as a…
  crossref Binod Bhattarai, Bibek Niroula, Aavash Chhetri, Kiran Raj Pandey 等 2026-05-06T07:02:02Z 置信度 0.70
• Legal and Policy Considerations in Adversarial Federated Learning
  A conflict exists between the adversarial vulnerabilities of federated learning and existing accountability and liability regulations in data protection and AI law. This chapter analyses GDPR, the EU AI Act, HIPAA, India's DPDP Act, and UAE ADHICS, along with …
  crossref Farouq Saber Al-Shibli, Ibrar Ahmad, Aftab Haider, Udit Mamodiya 等 2026-02-20T15:26:17Z 置信度 0.70
• FiZK: Federated Integrity with Zero Knowledge Hierarchical Federated Learning with ZK-Firewall Defense and IVC Proof Folding
  crossref Nevin Oommen, Atharva Naitam, Ayush Kshirsagar, Atharva Bhede 等 2026-07-21T19:10:50Z 置信度 0.70
• Blockchain Integration for Enhanced Trust and Security in Federated Learning for Healthcare 5.0
  crossref Sourav Kayal, Amit Kumar Rana, Sanjib Kundu 2026-02-06T05:46:47Z 置信度 0.70
• P2PFL: A Modular Framework for Decentralized Federated Learning
  Decentralized Federated Learning (DFL) offers a promising paradigm for collaborative machine learning training without centralized coordination, addressing critical limitations of centralized federated learning such as single points of failure, communication b…
  crossref Pedro Guijas, Sadi Alawadi, Daniel Rivero, Enrique Fernandez-Blanco 2026-05-17T00:37:01Z 置信度 0.70
• Federated learning-based collaborative assessment of distributed photovoltaic generation potential in multi-park systems
  This paper proposes a federated learning (FL) framework for the privacy-preserving, collaborative assessment of distributed photovoltaic (PV) generation potential across multiple industrial parks. The approach enables model training on decentralized datasets, …
  crossref Meng Chao Zhang 2026-08-14T14:55:28Z 置信度 0.70
• Cost-Constrained Incentive Mechanism for Federated Learning
  crossref Shihong Wu, Yuchuan Luo, Shaojing Fu, Ming Xu 等 2026-01-05T07:20:28Z 置信度 0.70
• CurFedSSL: Quality-Aware Curriculum Prototype Learning for Heterogeneous Federated Semi-Supervised Neural Networks
  Federated semi-supervised learning enables distributed neural networks to learn from limited labeled data and abundant unlabeled data without centralizing local datasets. However, heterogeneous client distributions make this setting vulnerable to unreliable pr…
  crossref Qingfei Wang, Xiaomiao Gui 2026-08-19T00:42:38Z 置信度 0.70
• FedFNR: Highly Robust Federated Learning Method for Label Noise
  Federated learning (FL) holds immense potential in privacy-preserving collaborative machine learning. However, ubiquitous label noise in decentralized data severely undermines the robustness of aggregated models, frequently misguiding optimization trajectories…
  crossref Huiqi Zhao, Mengqi Li, Gang Liu, Fang Fan 2026-03-09T20:46:02Z 置信度 0.70
• Federated Learning Frameworks for Privacy-Preserving Continuous Health Monitoring Using IoT Skin Patches
  Continuous health monitoring through IoT-enabled skin patches has emerged as a transformative approach in digital healthcare, enabling real-time acquisition of physiological and biochemical biomarkers such as glucose, lactate, cortisol, heart rate variability,…
  crossref Adedokun Taofeek, John Owen 2026-03-10T05:55:48Z 置信度 0.70
• PRISM-AMC: Replay-Free Continual Learning for Automatic Modulation Classification
  crossref Usman Akram, Matthew Graham, Haris Vikalo 2026-07-29T19:09:25Z 置信度 0.70
• Enhancing Federated Learning Robustness with Adaptive Client Aggregation for Heterogeneous Data
  crossref Gagandeep Singh 2026-05-16T12:18:35Z 置信度 0.70
• Understanding Communication Backends in Cross-Silo Federated Learning
  crossref Amir Ziashahabi, Chaoyang He, Salman Avestimehr 2026-07-14T19:38:09Z 置信度 0.70
• HeteroSense-FL: A Multimodal Simulation Testbed for Modality-Heterogeneous Federated Learning
  HeteroSense-FL is a Python software package for structured multimodal sensor simulation targeting modality-heterogeneous federated learning (FL) research. It generates 3D LiDAR point clouds and bed pressure maps across N client sites, each configured with a di…
  crossref Xun Shao, Kohsuke Yamakawa, Otani Aoba 2026-04-07T03:50:32Z 置信度 0.70
• TrusTEE: Storage-Centric Secure Federated Learning with Trusted Execution and Policy Enforcement
  crossref George Popescu-Craiova, Maribel Fernandez 2026-07-20T06:12:52Z 置信度 0.70
• Enhancing Federated Learning Robustness Against Adversarial Attacks Using SMPC
  Federated Learning (FL) allows for distributed model training across a population of devices while also offering the potential for privacy preservation, however, it remains susceptible to a range of adversarial manoeuvres, including model poisoning, backdoor a…
  crossref Sumit Kumar Kapoor, Kriti Sankhla, Saurabh Shandilya, Satya Prakash Awasthi 2026-02-20T15:26:17Z 置信度 0.70
• Blockchain-Secured Federated Learning with SMPC for Poisoning Attack Mitigation in Healthcare
  crossref Manukonda Likhith Naveen Reddy, Jeffrin Hannah 2026-03-14T11:59:56Z 置信度 0.70
• HomeGuard: Community-Driven Hierarchical Federated Learning for Robust Smart-Home Intrusion Detection
  crossref Philipp Eichhammer, Christian Berger, Hans Reiser 2026-07-23T10:16:50Z 置信度 0.70
• Deserialization Attacks in Federated Learning
  crossref Spaarsh Thakkar, Dev Gupta, Rajeev Shorey 2026-03-10T19:50:41Z 置信度 0.70
• Securing Financial Applications With Federated Learning Against Adversarial Threat
  The protection of sensitive data and digital transactions in the financial sector is increasingly under fire from sophisticated adversarial threats. With artificial intelligence (AI) -enabled fraud detection, credit scoring, and payment authentication, adversa…
  crossref Lingala Thirupathi, Sreeja Ravula, Nookala Shreya, Vennela Appala 等 2026-02-20T15:26:17Z 置信度 0.70
• Parameter-Efficient Federated Multimodal Alignment Tuning for Heterogeneous Clients
  crossref Xu Tan 2026-03-31T19:49:32Z 置信度 0.70
• FIDELIS: Blockchain-Enabled Protection against Poisoning Attacks in Federated Learning
  crossref Jane Carney, Kushal Upreti, Gaby Dagher, Tim Andersen 2026-03-08T10:53:27Z 置信度 0.70
• Managing Climate Variability with Federated Artificial Intelligence Models
  crossref A. Vegi Fernando, Mitha Guru, Sugandha Saxena, Mude Nagarjuna Naik 等 2026-06-23T12:51:06Z 置信度 0.70
• Towards Secure and Efficient Federated Learning for Wheat and Rice Disease Diagnosis
  crossref Shabad Kaur, Amandeep Kaur Virk 2026-07-13T08:58:02Z 置信度 0.70
• Agentic AI and Federated Learning for Privacy-Preserving Dental Imaging
  crossref Rajesh Lingam 2026-04-23T19:57:06Z 置信度 0.70
• A federated learning-enhanced sharded blockchain framework for privacy-enhanced authentication in IoT E-learning systems
  Abstract The accelerated growth of IoT-facilitated e-learning ecosystems has introduced significant challenges for secure, scalable, and privacy-aware user authentication. Existing approaches face a fundamental trade-off: conventional blockchain systems often …
  crossref Zhonghao Dong 2026-08-10T06:52:44Z 置信度 0.70
• Work-in-Progress on Federated and Cluster-Aware Wi-Fi Fingerprinting for Indoor Localisation
  crossref Celia Saenz-Martinez, Carlos Granell, Miguel Matey, Sergio Trilles 2026-07-29T19:12:10Z 置信度 0.70
• Secure Federated Learning Under Non-IID and Multimodal Breast Imaging
  crossref Ashika Sameem Abdul Rasheed, Mamoun Awad, Mohammad Mehedy Masud 2026-04-20T20:01:37Z 置信度 0.70
• Privacy-Aware Federated Learning Architectures for Cross-Institutional Healthcare Collaboration
  The chapter explores Privacy-Aware Federated Learning (FL) architectures that enable secure, collaborative healthcare model training without sharing raw patient data. It highlights how Differential Privacy, Secure Multi-Party Computation, and Homomorphic Encry…
  crossref S. Aarthi, Jaypalsinh A. Gohil 2026-06-11T20:45:37Z 置信度 0.70
• Customer Churn Prediction in Banking Using Federated Learning
  crossref Kamatchi K, Yerrajodu Kartheek Kumar, Midde Venkatesh 2026-04-30T19:45:47Z 置信度 0.70
• Relay Selection Using Federated Reinforcement Learning
  crossref Ishita Barui, Poonam Jindal 2026-07-21T19:05:14Z 置信度 0.70
• Federated IAM for Critical Systems: A Blockchain-Based Decentralized Identity Framework Enhanced by Federated Learning for Cross-Sector CNI Trust
  crossref Eria Pinyi 2026-01-06T16:20:06Z 置信度 0.70
• Privacy-Preserving Federated Learning for Multi-Tenant CRM Systems
  crossref Nidhi Sharma 2026-02-23T20:46:21Z 置信度 0.70
• Collaborative modeling of educational data in learning cities based on federated learning
  crossref Liang Bian 2026-06-12T16:06:15Z 置信度 0.70
• FedSplitX: Federated Split Learning for Computationally-Constrained Heterogeneous Clients
  crossref Jiyun Shin, Honggu Kang, Seongah Jeong, Jin-Hyun Ahn 2026-01-05T12:26:21Z 置信度 0.70
• Harmonizing Spectral Style and Semantic Prior for Generalizable Federated Learning
  Federated domain generalization (FedDG) aims to learn a global model from multiple decentralized source domains that can generalize to unseen domains under privacy constraints. A prevailing line of FedDG research attempts to improve generalization by diversify…
  crossref Yue Yang, Fan Ma 2026-02-12T08:37:06Z 置信度 0.70
• A Hybrid Homomorphic Federated Learning Framework with Adaptive Aggregation for Image Classification
  Federated learning enables collaborative machine learning while preserving data privacy, yet existing approaches struggle to simultaneously achieve privacy protection, computational efficiency, and model accuracy. We propose HHFL-AA (Hybrid Homomorphic Federat…
  crossref Anan Shi 2026-04-21T16:33:54Z 置信度 0.70
• Middleware Development for Secure Federated Learning in Healthcare Environments
  crossref Silvia Lorero, Mario Muñoz Organero 2026-01-26T16:39:18Z 置信度 0.70
• Quantum threats and federated AI
  crossref R. Shyam, C. V. Swetha, J. Jesupriya 2026-03-25T15:39:21Z 置信度 0.70
• Leveraging Secure Federated Learning for Data-Driven Business Decisions
  In the age of data-based business, organizations depend more and more on wide analysis to inform strategic decisions. However, the growing concerns around data privacy, regulatory compliance, and inter-cooperation are significant challenges. This chapter explo…
  crossref Udit Mamodiya, Randhir Singh Baghel 2026-02-20T15:26:17Z 置信度 0.70
• Federated Learning for Privacy-Preserving Next-Generation AI: Architectures, Security and Digital Transformation
  Industrial systems and the world of Internet of Things, concerns about data privacy, security and regulatory compliance have grown, when artificial intelligence has been rapidly expanding into healthcare. This chapter presents a comprehensive review of existin…
  crossref Sultan Ahmad, Oroos Zohra, Sarah Raza, Mohammed Alimul Haque 等 2026-06-03T13:27:32Z 置信度 0.70