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• Economics and Equity of Large Language Models: Health Care Perspective.
  europepmc 2024 置信度 0.80
• Federated quantum-inspired anomaly detection using collaborative neural clients.
  europepmc 2025 置信度 0.80
• Learning across diverse biomedical data modalities and cohorts: Challenges and opportunities for innovation.
  europepmc 2024 置信度 0.80
• Efficient privacy-preserving variable-length substring match for genome sequence.
  europepmc 2022 置信度 0.80
• A literature review of "lawful" text and data mining.
  europepmc 2024 置信度 0.80
• GraphRAG-Enabled Local Large Language Model for Gestational Diabetes Mellitus: Development of a Proof-of-Concept.
  europepmc 2026 置信度 0.80
• Use Cases Requiring Privacy-Preserving Record Linkage in Paediatric Oncology.
  europepmc 2024 置信度 0.80
• Efficient federated learning for distributed neuroimaging data.
  europepmc 2024 置信度 0.80
• Split Learning for Distributed Collaborative Training of Deep Learning Models in Health Informatics.
  europepmc 2023 置信度 0.80
• IoT-based bed and ventilator management system during the COVID-19 pandemic.
  europepmc 2025 置信度 0.80
• Dual-hybrid intrusion detection system to detect False Data Injection in smart grids.
  europepmc 2025 置信度 0.80
• Cloud-native simulation framework for gossip protocol: Modeling and analyzing network dynamics.
  europepmc 2025 置信度 0.80
• CRISPR and Artificial Intelligence in Neuroregeneration: Closed-Loop Strategies for Precision Medicine, Spinal Cord Repair, and Adaptive Neuro-Oncology.
  europepmc 2025 置信度 0.80
• DecentralDC: Assessing data contribution under decentralized sharing and exchange blockchain.
  europepmc 2024 置信度 0.80
• A System Model and Requirements for Transformation to Human-Centric Digital Health.
  europepmc 2025 置信度 0.80
• Privacy Protection and Secondary Use of Health Data: Strategies and Methods.
  europepmc 2021 置信度 0.80
• Enhanced Network Intrusion Detection System for Internet of Things Security Using Multimodal Big Data Representation with Transfer Learning and Game Theory.
  europepmc 2024 置信度 0.80
• Health data space nodes for privacy-preserving linkage of medical data to support collaborative secondary analyses.
  europepmc 2024 置信度 0.80
• Issues and Limitations on the Road to Fair and Inclusive AI Solutions for Biomedical Challenges.
  europepmc 2025 置信度 0.80
• Generative artificial intelligence in animal genomics for smart agriculture: Applications, challenges, and future prospects.
  Generative artificial intelligence (AI) is becoming a groundbreaking paradigm in the field of animal genomics and is providing the possibility to take a step towards intelligent agriculture, with better data integration, predictive modeling, and biological des…
  europepmc 2026 置信度 0.80
• Optimal Offline ORAM with Perfect Security via Simple Oblivious Priority Queues
  Oblivious RAM (ORAM) is a well-researched primitive to hide the memory access pattern of a RAM computation; it has a variety of applications in trusted computing, outsourced storage, and multiparty computation. In this paper, we study the so-called offline ORA…
  datacite Thießen, Thore, Vahrenhold, Jan 2024 置信度 0.66 Data Structures and Algorithms (cs.DS)Cryptography and Security (cs.CR)FOS: Computer and information sciencesFOS: Computer and information sciences
• Optimal Offline ORAM with Perfect Security via Simple Oblivious Priority Queues
  Oblivious RAM (ORAM) is a well-researched primitive to hide the memory access pattern of a RAM computation; it has a variety of applications in trusted computing, outsourced storage, and multiparty computation. In this paper, we study the so-called offline ORA…
  datacite Thießen, Thore, Vahrenhold, Jan 2024 置信度 0.66 offline ORAMoblivious priority queueperfect securityexternal memory algorithmcache-oblivious algorithm
• Secure Multiparty Computation of Symmetric Functions with Polylogarithmic Bottleneck Complexity and Correlated Randomness
  Bottleneck complexity is an efficiency measure of secure multiparty computation (MPC) protocols introduced to achieve load-balancing in large-scale networks, which is defined as the maximum communication complexity required by any one player within the protoco…
  datacite Eriguchi, Reo 2024 置信度 0.66 Secure multiparty computationBottleneck complexitySecret sharingSecurity and privacy → Information-theoretic techniques
• Pencil: Private and Extensible Collaborative Learning without the Non-Colluding Assumption
  The escalating focus on data privacy poses significant challenges for collaborative neural network training, where data ownership and model training/deployment responsibilities reside with distinct entities. Our community has made substantial contributions to …
  datacite Liu, Xuanqi, Liu, Zhuotao, Li, Qi, Xu, Ke 等 2024 置信度 0.66 Cryptography and Security (cs.CR)Machine Learning (cs.LG)FOS: Computer and information sciences
• I can't see it but I can Fine-tune it: On Encrypted Fine-tuning of Transformers using Fully Homomorphic Encryption
  In today's machine learning landscape, fine-tuning pretrained transformer models has emerged as an essential technique, particularly in scenarios where access to task-aligned training data is limited. However, challenges surface when data sharing encounters ob…
  datacite Panzade, Prajwal, Takabi, Daniel, Cai, Zhipeng 2024 置信度 0.66 Machine Learning (cs.LG)Artificial Intelligence (cs.AI)Cryptography and Security (cs.CR)FOS: Computer and information sciences
• Privacy-Preserving Face Recognition in Hybrid Frequency-Color Domain
  Face recognition technology has been deployed in various real-life applications. The most sophisticated deep learning-based face recognition systems rely on training millions of face images through complex deep neural networks to achieve high accuracy. It is q…
  datacite Han, Dong, Li, Yong, Denzler, Joachim 2024 置信度 0.66 Computer Vision and Pattern Recognition (cs.CV)FOS: Computer and information sciencesFOS: Computer and information sciences
• Data-driven predictive maintenance of induction motors using self-supervised and federated learning on noisy current and vibration signals.
  Induction motors (IMs) sustain a vast share of industrial activity but are prone to bearing wear, rotor-bar breakage, eccentricity, and insulation defects that emerge under non-stationary, noisy conditions. While Motor Current Signature Analysis (MCSA) remains…
  pubmed Gopalakrishnan T, Shanmugasundaram N, Simon D, Chan CK 等 2026 Apr 6 置信度 0.82
• FedKGC: Federated Knowledge-Grounded Calibration Framework for Hallucination Mitigation in Medical LLMs.
  The integration of Large Language Models (LLMs) into clinical workflows offers transformative potential for decision support and documentation, yet widespread adoption is currently stalled by the prohibitive risks of hallucination and strict privacy regulation…
  pubmed Chakraborty C, Othman SB, Guduri M, Frikha MA 2026 Apr 6 置信度 0.82
• Harnessing artificial intelligence to decode the rhizosphere microbiome.
  The rhizosphere microbiome plays crucial roles in plant health by regulating nutrient cycling and enhancing stress resilience. However, due to its complexity, the rhizosphere microbiome is quite challenging to analyze using conventional approaches. Recent adva…
  pubmed Ma J, Qiao J, Cao Y, Cheng Z 2026 Mar 置信度 0.82
• The role of artificial intelligence in early detection and risk prediction of ischemic heart disease.
  Ischemic heart disease (IHD) remains a leading cause of global morbidity and mortality, underscoring the need for rapid and accurate diagnostic strategies. Conventional methods, including electrocardiography (ECG), imaging, and biomarkers, are effective but li…
  pubmed Azami P, Kojuri J, Razeghian-Jahromi I 2026 Apr 置信度 0.82
• The Latent Yield: A Formal Framework for Low-Inhibition Cognitive Workspace Architecture, Privacy-Preserving Multi-Modal Feature Projection, and the Mapping of Human Latent Capacity in Decentralized, Peer-to-Peer Sovereign Data Nodes
  This paper presents a theoretical framework and enabling specification for a low-inhibition associative cognitive workspace, designated the Dream Galaxy, implemented within decentralized, peer-to-peer sovereign data nodes constituting a federated personal cogn…
  datacite Hoppe, Eric Christian 2026 置信度 0.66 ow-inhibition cognitive workspacepersistent homologyasynchronous local topological optimizationprivacy-preserving multi-modal feature projectionsub-threshold physiological tokenization
• The Latent Yield: A Formal Framework for Low-Inhibition Cognitive Workspace Architecture, Privacy-Preserving Multi-Modal Feature Projection, and the Mapping of Human Latent Capacity in Decentralized, Peer-to-Peer Sovereign Data Nodes
  This paper presents a theoretical framework and enabling specification for a low-inhibition associative cognitive workspace, designated the Dream Galaxy, implemented within decentralized, peer-to-peer sovereign data nodes constituting a federated personal cogn…
  datacite Hoppe, Eric Christian 2026 置信度 0.66 ow-inhibition cognitive workspacepersistent homologyasynchronous local topological optimizationprivacy-preserving multi-modal feature projectionsub-threshold physiological tokenization
• Industrialisation des thérapies décentralisées : Biosécurité de l'oncologie personnalisée à ARNm (Déclaration d'antériorité / Prior Art)
  Résumé FRCe document, produit avec l’assistance de Gemini 3 Raisonnement, est publié sous Licence Apache 2.0. Il constitue une publication défensive (antériorité) volontaire 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 B01L 3/00B01L 3/5027C12Q 1/6886G16H 50/20G16H 20/17
• Industrialisation des thérapies décentralisées : Biosécurité de l'oncologie personnalisée à ARNm (Déclaration d'antériorité / Prior Art)
  Résumé FRCe document, produit avec l’assistance de Gemini 3 Raisonnement, est publié sous Licence Apache 2.0. Il constitue une publication défensive (antériorité) volontaire 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 B01L 3/00B01L 3/5027C12Q 1/6886G16H 50/20G16H 20/17
• Designing the Future of Secure Intelligence: A Vision for AI-Enabled Cyber-Resilient Ecosystems
  This vision paper presents a consolidated perspective developed through six European initiatives—VIGILANCE, CyberAId, ENFORCE, ACT4FOOD, CTIS4NIS, and 5G-TACTIC—working together through the Cyber-Resilience Synergy Network (CRSN). It examines how Artificial In…
  datacite Gizelis, Christos A., Marinakis, Achilleas, Papamokos, Stavros, Kefalogiannis, Michalis 等 2026 置信度 0.66 Artificial IntelligenceCybersecurityCyber resilienceTrustworthy AICyber-Resilience Synergy Network
• Designing the Future of Secure Intelligence: A Vision for AI-Enabled Cyber-Resilient Ecosystems
  This vision paper presents a consolidated perspective developed through six European initiatives—VIGILANCE, CyberAId, ENFORCE, ACT4FOOD, CTIS4NIS, and 5G-TACTIC—working together through the Cyber-Resilience Synergy Network (CRSN). It examines how Artificial In…
  datacite Gizelis, Christos A., Marinakis, Achilleas, Papamokos, Stavros, Kefalogiannis, Michalis 等 2026 置信度 0.66 Artificial IntelligenceCybersecurityCyber resilienceTrustworthy AICyber-Resilience Synergy Network
• NFSA: Non-Forward Secure Aggregation with One Server via Two Layer Secret Sharing
  Federated Learning (FL) enables collaborative model training while preserving privacy by keeping data local. However, the risk of sensitive data leakage through model updates necessitates the use of secure aggregation protocols. Existing server-based secure ag…
  datacite Zhou, Yufei 2026 置信度 0.66 Cryptography and Security (cs.CR)FOS: Computer and information sciences
• EdgeFaaS: A Function-based Framework for Edge Computing
  Edge computing brings unique challenges as the resources on the edge are highly diverse in capabilities and capacities, and highly distributed across many users and the physical world. Existing distributed computing frameworks cannot adequately handle this lev…
  datacite Vadnere, Neha, Wang, Yu-Ting, Chen, Yitao, Sadesh, Sreehari 等 2026 置信度 0.66 Distributed, Parallel, and Cluster Computing (cs.DC)Artificial Intelligence (cs.AI)FOS: Computer and information sciences
• Industrialisation des thérapies décentralisées : Biosécurité de l'oncologie personnalisée à ARNm (Déclaration d'antériorité / Prior Art)
  Résumé FRCe document, produit avec l’assistance de Gemini 3 Raisonnement, est publié sous Licence Apache 2.0. Il constitue une publication défensive (antériorité) volontaire 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 B01L 3/00B01L 3/5027C12Q 1/6886G16H 50/20G16H 20/17
• PriEval-Protect: A Unified Framework for Privacy Evaluation and Protection in Healthcare Systems
  Safeguarding patient privacy while enabling meaningful healthcare data use remains critical under GDPR and HIPAA. Existing compliance methods are manual, error-prone, and separate policy audits from data-level assessments. This paper presents PriEval-Protect, …
  datacite Chebil, Ilef, Hadj, Asma El, Yousfi, Souheib, Hedhili, Aroua 等 2026 置信度 0.66 Cryptography and Security (cs.CR)FOS: Computer and information sciences
• FM$^2$: Unified Federated Foundation Models for Heterogeneous Multimodal Medical Imaging
  Building foundation models for medical imaging requires pooling data across institutions, yet privacy regulations prohibit centralized aggregation. Existing Federated Foundation Models either fine-tune natural-image models with poor medical-domain transfer, or…
  datacite Chen, Shengchao, Shu, Ting 2026 置信度 0.66 Computer Vision and Pattern Recognition (cs.CV)FOS: Computer and information sciences
• First-Order Softmax Weighted Switching Gradient Method for Distributed Stochastic Minimax Optimization with Stochastic Constraints
  This paper addresses the distributed stochastic minimax optimization problem subject to stochastic constraints. We propose a novel first-order Softmax-Weighted Switching Gradient method tailored for federated learning. Under full client participation, our algo…
  datacite Luo, Zhankun, Upadhyay, Antesh, Moon, Sang Bin, Hashemi, Abolfazl 2026 置信度 0.66 Machine Learning (cs.LG)Distributed, Parallel, and Cluster Computing (cs.DC)FOS: Computer and information sciences
• Multi-Omics Machine Learning Models for Precision Cancer Prognostication
  Background: Cancer remains a leading cause of mortality worldwide, necessitating accurate prognostic tools to guide clinical decision-making and personalized treatment strategies. Traditional prognostic models based on clinicopathological features demonstrate …
  datacite Vikram Patel*1, Ananya George2, Manish Khanna3, Shatrughna Nagrik4 2026 置信度 0.66 Multi-omics, machine learning, deep learning, cancer prognosis, precision oncology, data integration, survival prediction.
• Multi-Omics Machine Learning Models for Precision Cancer Prognostication
  Background: Cancer remains a leading cause of mortality worldwide, necessitating accurate prognostic tools to guide clinical decision-making and personalized treatment strategies. Traditional prognostic models based on clinicopathological features demonstrate …
  datacite Vikram Patel*1, Ananya George2, Manish Khanna3, Shatrughna Nagrik4 2026 置信度 0.66 Multi-omics, machine learning, deep learning, cancer prognosis, precision oncology, data integration, survival prediction.
• FedOPAL: One-Shot Federated Learning via Analytic Visual Prompt Tuning
  With the widespread deployment of basic models in edge intelligence, communication bandwidth has become a core bottleneck restricting the scalability of federated learning. Although one-shot federated learning alleviates this problem by minimizing communicatio…
  datacite Qiu, Lingyu, Annunziata, Daniela, Izzo, Stefano, Giampaolo, Fabio 等 2026 置信度 0.66 Artificial Intelligence (cs.AI)FOS: Computer and information sciences
• Decoding Tears: A Review of the Biology of Crying and Machine Learning Approaches to Infant Cry Classification
  Crying spans at least three distinct physiological pathways in adults and serves as the primary communication channel for pre-verbal infants. Although the physiology of human crying and machine learning-based infant cry analysis have each been studied extensiv…
  datacite Pathan, Mohammed Akram Khan 2026 置信度 0.66 Infant Cry ClassificationMachine LearningMFCCEmotional TearsAudio Signal Processing
• Decoding Tears: A Review of the Biology of Crying and Machine Learning Approaches to Infant Cry Classification
  Crying spans at least three distinct physiological pathways in adults and serves as the primary communication channel for pre-verbal infants. Although the physiology of human crying and machine learning-based infant cry analysis have each been studied extensiv…
  datacite Pathan, Mohammed Akram Khan 2026 置信度 0.66 Infant Cry ClassificationMachine LearningMFCCEmotional TearsAudio Signal Processing
• Decoding Tears: A Review of the Biology of Crying and Machine Learning Approaches to Infant Cry Classification
  Crying spans at least three distinct physiological pathways in adults and serves as the primary communication channel for pre-verbal infants. Although the physiology of human crying and machine learning-based infant cry analysis have each been studied extensiv…
  datacite Pathan, Mohammed Akram Khan 2026 置信度 0.66 Infant Cry ClassificationMachine LearningMFCCEmotional TearsAudio Signal Processing
• An Adaptive Differentially Private Federated Learning Framework
  Federated learning enables collaborative model training across distributed clients while preserving data privacy. However, in practical deployments, device heterogeneity and non-independent and identically distributed (Non-IID) data often lead to unstable and …
  datacite Wang, Jin, Ma, Hui, Zhang, Yajun, Pei, Xinjun 等 2026 置信度 0.66 Artificial Intelligence (cs.AI)FOS: Computer and information sciences
• Inertia-Informed Federated Learning Control Framework for Distributed Smart Grid Resilience
  Resilient-by-design smart grid control demands frameworks capable of maintaining stability under physical disturbances and communication failures, without reliance on centralized coordination. While Centralized Training Decentralized Execution (CTDE) enables a…
  datacite Shahbaz, Ibrahim, Al-Refai, Omar, Hammad, Eman 2026 置信度 0.66 Systems and Control (eess.SY)FOS: Electrical engineering, electronic engineering, information engineering
• Federated Physics-Grounded Reinforcement Learning for Distributed Stability Control in Smart Grids
  Transient stability control in smart grids requires rapid post-fault damping of generator frequency and rotor angle deviations to prevent cascading failures. This paper proposes FedPPO-PG, a Federated Multi-Agent Proximal Policy Optimization framework with Phy…
  datacite Al-Refai, Omar, Shahbaz, Ibrahim, Husseinat, Adam Ali, Hammad, Eman 2026 置信度 0.66 Machine Learning (cs.LG)Systems and Control (eess.SY)FOS: Computer and information sciencesFOS: Electrical engineering, electronic engineering, information engineering
• pFedNavi: Structure-Aware Personalized Federated Vision-Language Navigation for Embodied AI
  Vision-Language Navigation VLN requires large-scale trajectory instruction data from private indoor environments, raising significant privacy concerns. Federated Learning FL mitigates this by keeping data on-device, but vanilla FL struggles under VLNs' extreme…
  datacite Yang, Qingqian, Wang, Hao, Zhang, Sai Qian, Li, Jian 等 2026 置信度 0.66 Computer Vision and Pattern Recognition (cs.CV)Artificial Intelligence (cs.AI)FOS: Computer and information sciences
• GDBR: Label Recovery Attack Against Partial Gradient Encryption in Federated Learning
  The increasing demand for data privacy, alongside the benefits of aggregating data from networked devices, has catalyzed the emergence of federated learning (FL). In FL, clients jointly train a global model by sharing gradients computed over private data. Whil…
  datacite Zhang, Rui, Chow, Ka-Ho 2024 置信度 0.66 Machine Learning (cs.LG)Cryptography and Security (cs.CR)FOS: Computer and information sciences
• CDSA-ATM: Air Traffic Management Pillar of the CDSA Federated Reinforcement Learning Paradigm — Multi-modal Trajectory + ATS + ANSP Fusion
  CDSA-ATM is the reference implementation of the air traffic management pillar of the Complementary Diagnostic Safety Approach (CDSA), a third-generation aviation safety paradigm formalised as a Federated Reinforcement Learning (FRL) framework (Scenario C decis…
  datacite Cantekin, Mete 2026 置信度 0.66 federated reinforcement learningair traffic managementADS-BOpenSky NetworkATS occurrences
• CDSA-MRO: Maintenance Safety Pillar of the CDSA Federated Reinforcement Learning Paradigm — Multi-modal Engine + Cyber + Maintenance Fusion
  CDSA-MRO is the reference implementation of the maintenance safety 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,…
  datacite Cantekin, Mete 2026 置信度 0.66 reinforcement learningsynthetic datacyber safetyaviation maintenancedata locality
• CDSA-MRO: Maintenance Safety Pillar of the CDSA Federated Reinforcement Learning Paradigm — Multi-modal Engine + Cyber + Maintenance Fusion
  CDSA-MRO is the reference implementation of the maintenance safety 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,…
  datacite Cantekin, Mete 2026 置信度 0.66 reinforcement learningsynthetic datacyber safetyaviation maintenancedata locality
• One Token Across Twenty Orders of Magnitude: Where a Single-Token Class-Discriminant Codebook Works, Where It Does Not, and When to Add a Co-Channel
  Description This record accompanies a manuscript that asks one question at extreme breadth: can a single compact, on-device token — one window of a signal reduced to a single ~6-9-bit class-discriminant codebook index — carry a decision across sensing modaliti…
  datacite Ferlic, Randolph James, Ferlic, Kimberly Kate 2026 置信度 0.66 class-discriminant codebookvector quantizationon-device inferenceedge AIcross-scale sensing
• One Token Across Twenty Orders of Magnitude: Where a Single-Token Class-Discriminant Codebook Works, Where It Does Not, and When to Add a Co-Channel
  Description This record accompanies a manuscript that asks one question at extreme breadth: can a single compact, on-device token — one window of a signal reduced to a single ~6-9-bit class-discriminant codebook index — carry a decision across sensing modaliti…
  datacite Ferlic, Randolph James, Ferlic, Kimberly Kate 2026 置信度 0.66 class-discriminant codebookvector quantizationon-device inferenceedge AIcross-scale sensing
• Benchmarking Federated Learning and Knowledge Distillation for Point Cloud Classification
  Deploying 3D point cloud analysis in privacy-sensitive, resource-constrained settings faces two barriers: data cannot be centralized, and models must run on limited edge hardware. We present a multi-seed benchmark jointly evaluating federated learning (FL) and…
  datacite Aiersilan, Aizierjiang 2026 置信度 0.66 Graphics (cs.GR)Artificial Intelligence (cs.AI)Computer Vision and Pattern Recognition (cs.CV)Distributed, Parallel, and Cluster Computing (cs.DC)Machine Learning (cs.LG)
• On the Detectability of Active Gradient Inversion Attacks in Federated Learning (IEEE S&P '26) - Source Code
  This artifact accompanies the paper "On the Detectability of Active Gradient Inversion Attacks in Federated Learning," published at the 2026 IEEE Symposium on Security and Privacy (SP). Federated Learning (FL) allows multiple clients to collaboratively train a…
  datacite Parrella, Giuseppe, Mazzocca, Carlo, FOGGIA, PASQUALE, Carletti, Vincenzo 等 2026 置信度 0.66
• On the Detectability of Active Gradient Inversion Attacks in Federated Learning (IEEE S&P '26) - Source Code
  This artifact accompanies the paper "On the Detectability of Active Gradient Inversion Attacks in Federated Learning," published at the 2026 IEEE Symposium on Security and Privacy (SP). Federated Learning (FL) allows multiple clients to collaboratively train a…
  datacite Parrella, Giuseppe, Mazzocca, Carlo, FOGGIA, PASQUALE, Carletti, Vincenzo 等 2026 置信度 0.66
• Personalized Additive Modeling for Multi-level Federated Learning
  Contemporary AI faces the challenge of balancing generality with user-specific personalization. In federated learning (FL), this challenge is amplified by highly heterogeneous client data with complex non-IID patterns beyond standard IID assumptions. Many exis…
  datacite Chen, Shutong, Long, Guodong, Zhou, Tianyi, Ma, Jie 等 2024 置信度 0.66 Machine Learning (cs.LG)FOS: Computer and information sciences
• Federated Learning with Energy-Based Structured Probabilistic Inference
  Federated learning typically aggregates client updates using fixed or heuristic weighting rules, which can be suboptimal when clients have heterogeneous data and varying contributions to the global model. We propose a framework that refines client aggregation …
  datacite Fenoglio, Dario, Kirilenko, Daniil, Gjoreski, Martin, Langheinrich, Marc 2026 置信度 0.66 Machine Learning (cs.LG)Artificial Intelligence (cs.AI)FOS: Computer and information sciences
• The Role of Artificial Intelligence in the SKA Era
  The Square Kilometre Array Observatory (SKAO) will usher in an era of unprecedented data complexity and scientific opportunity in radio astronomy, producing petabyte-scale datasets and terabit-per-second streams that challenge traditional analysis paradigms. A…
  datacite Denzel, Philipp, Schilling, Frank-Peter, Gavagnin, Elena 2026 置信度 0.66 Instrumentation and Methods for Astrophysics (astro-ph.IM)FOS: Physical sciences
• When the Aggregator Cheats: Data-Free Backdoors in Federated LLM-based QA Systems
  Large Language Model (LLM)-based question-answering (QA) systems are increasingly deployed in sensitive domains such as healthcare, mental health counseling, and legal consultation. Federated learning (FL) enables collaborative training without sharing raw cli…
  datacite Zhu, Chenqing, Dai, Yanbo, Tian, Yulong, Li, Qingming 等 2026 置信度 0.66 Cryptography and Security (cs.CR)FOS: Computer and information sciences
• AI in Predictive Healthcare: How Artificial Intelligence and Wearable Devices are Transforming Early Disease Detection | A Systematic Literature Review (2026) | Ruchir Ganatra
  This research paper was prepared by Ruchir Ganatra as an independent research study exploring Artificial Intelligence in Predictive Healthcare. The paper provides a comprehensive review of AI-powered wearable devices, machine learning algorithms, digital healt…
  datacite Ganatra, Ruchir 2026 置信度 0.66 Artificial IntelligenceMachine LearningDigital HealthBiomedical EngineeringMedical Informatics
• AI in Predictive Healthcare: How Artificial Intelligence and Wearable Devices are Transforming Early Disease Detection | A Systematic Literature Review (2026) | Ruchir Ganatra
  This research paper was prepared by Ruchir Ganatra as an independent research study exploring Artificial Intelligence in Predictive Healthcare. The paper provides a comprehensive review of AI-powered wearable devices, machine learning algorithms, digital healt…
  datacite Ganatra, Ruchir 2026 置信度 0.66 Artificial IntelligenceMachine LearningDigital HealthBiomedical EngineeringMedical Informatics
• ОБЪЯСНИМЫЕ И БЕЗОПАСНЫЕ ПЛАТФОРМЫ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА ДЛЯ ГЕНОМИКИ CRISPR: ДОСТИЖЕНИЯ, ОГРАНИЧЕНИЯ, СИСТЕМЫ МОДЕЛИРОВАНИЯ И ТРАНСЛЯЦИОННАЯ ИНТЕГРАЦИЯ НА ОСНОВЕ БЛОКЧЕЙНА
  Искусственный интеллект (ИИ) значительно ускорил процесс редактирования генома с помощью CRISPR-Cas за счет совершенствования проектирования направляющей РНК (gRNA), прогнозирования нецелевых эффектов, оценки результатов репарации ДНК и оптимизации метода прай…
  datacite Velevela Raghu Ram Chowdary -., Raja Rao M. 2026 置信度 0.66 CRISPRExplainable AIGenome EditingBlockchainDNA Repair Simulation
• МУЛЬТИОМИЧЕСКИЙ ИИ ДЛЯ ПЕРСОНАЛИЗИРОВАННОЙ ИНТЕНСИВНОЙ ТЕРАПИИ: ОБЗОР ЦИФРОВЫХ ДВОЙНИКОВ, CRISPR И ОБЪЯСНИМЫХ СИСТЕМ
  Персонализированная медицина меняет ситуацию, переходя от универсальных методов лечения к стратегиям, учитывающим уникальные биологические особенности каждого человека. Этот сдвиг имеет огромное значение в интенсивной терапии, где пациенты находятся в уязвимом…
  datacite Velevela R., Raja Rao M. 2026 置信度 0.66 personalized medicinemulti-omicsartificial intelligencedigital twinsCRISPR
• Ep. 128: AI's Dial-Up Era: Looking Back from 2036
  Episode summary: In this forward-thinking episode of My Weird Prompts, hosts Herman Poppleberry and Corn kick off the year 2026 by traveling a decade into the future. They imagine a world in 2036 where the "cutting-edge" AI of today is viewed as an adorable, c…
  datacite Rosehill, Daniel, Gemini 3.1 (Flash), Chatterbox TTS 2026 置信度 0.66 podcastai-generatedmy weird promptsfuture2036
• Chisme: Heterogeneity-Aware Gossip Learning
  As end-user device capability increases and demand for intelligent services at the Internet's edge rises, distributed learning has emerged as a key enabling technology for the intelligent edge. Existing approaches like federated learning (FL) and decentralized…
  datacite Kuttivelil, Harikrishna, Obraczka, Katia 2025 置信度 0.66 Machine Learning (cs.LG)Emerging Technologies (cs.ET)Multiagent Systems (cs.MA)Social and Information Networks (cs.SI)FOS: Computer and information sciences
• Performance and Energy Trade-Off Analysis of Hierarchical Federated Learning for Plant Disease Classification
  Early detection of plant diseases is critical for improving crop productivity, while it also facilitates the foundations of precision agriculture. Recent advances in distributed deep learning have enabled plant disease classification models to be trained acros…
  datacite Papanikolaou, Athanasios, Tziouvaras, Athanasios, Stoikos, Pavlos, Xenakis, Apostolos 等 2026 置信度 0.66 Distributed, Parallel, and Cluster Computing (cs.DC)Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• Color Matters: Trigger Color Affects Success in Federated Backdoor Attacks
  Federated learning is vulnerable to backdoor attacks in which malicious clients inject poisoned updates while preserving benign-task performance. In this paper, we study a semantics-driven backdoor mechanism in which attackers use natural visual accessories as…
  datacite Herath, Kavindu, Zhao, Joshua C., Bagchi, Saurabh 2026 置信度 0.66 Cryptography and Security (cs.CR)Artificial Intelligence (cs.AI)Computer Vision and Pattern Recognition (cs.CV)Machine Learning (cs.LG)FOS: Computer and information sciences
• GreenFLag: A Green Agentic Approach for Energy-Efficient Federated Learning
  This presentation done at WoWMoM 2026, showcases the paper "GreenFLag: A Green Agentic Approach for Energy-Efficient Federated Learning". It presents the system description of the paper, a visualization tool used for the simulation of the network and the work'…
  datacite Panagea, Theodora 2026 置信度 0.66
• GreenFLag: A Green Agentic Approach for Energy-Efficient Federated Learning
  This presentation done at WoWMoM 2026, showcases the paper "GreenFLag: A Green Agentic Approach for Energy-Efficient Federated Learning". It presents the system description of the paper, a visualization tool used for the simulation of the network and the work'…
  datacite Panagea, Theodora 2026 置信度 0.66
• Trust and Reputation in Federated Learning: A Systematic Literature Review of Mechanisms, Evaluation Practices, and Open Challenges
  This is a preprint. This paper has been submitted to ACM Computing Surveys and is currently under peer review.Trust and reputation mechanisms have emerged as a reliability-management layer for Federated Learning (FL), where clients may be heterogeneous, interm…
  datacite Rashid, Md Mamunur, Xiang, Yong, Uddin, Md Palash, Sood, Keshav 等 2026 置信度 0.66
• Trust and Reputation in Federated Learning: A Systematic Literature Review of Mechanisms, Evaluation Practices, and Open Challenges
  This is a preprint. This paper has been submitted to ACM Computing Surveys and is currently under peer review.Trust and reputation mechanisms have emerged as a reliability-management layer for Federated Learning (FL), where clients may be heterogeneous, interm…
  datacite Rashid, Md Mamunur, Xiang, Yong, Uddin, Md Palash, Sood, Keshav 等 2026 置信度 0.66
• FedSteer: Taming Extreme Gradient Staleness in Federated Learning with Corrective Projections and Caching
  Federated learning (FL) is often subject to aggregation variance if clients do not consistently participate in training rounds. While reusing stale model updates from inactive clients is a common technique to reduce this variance, we find that with skewed clie…
  datacite Zhang, Haoran, Pereira, Cainã Figueiredo, Siew, Marie, Liu, Xutong 等 2026 置信度 0.66 Machine Learning (cs.LG)Artificial Intelligence (cs.AI)FOS: Computer and information sciencesFOS: Computer and information sciencesI.2.11; I.2.7
• Locked Out of the Intelligence Revolution: Nigeria's Upstream Data Gap, the Billion-Dollar AI Opportunity, and a Technical Framework for Industry Data Collaboration in the Niger Delta
  Nigeria holds 37.01 billion barrels of proven crude oil and condensate reserves, yet its production has consistently fallen short of its OPEC quota, and the government's target of 3 million barrels per day by 2030 assumes operational capacity the country has n…
  datacite Ezeagu, Vera Ijeoma 2026 置信度 0.66 upstream data infrastructuredata collaboration frameworkNigerian petroleum industryartificial intelligencemachine learning
• Locked Out of the Intelligence Revolution: Nigeria's Upstream Data Gap, the Billion-Dollar AI Opportunity, and a Technical Framework for Industry Data Collaboration in the Niger Delta
  Nigeria holds 37.01 billion barrels of proven crude oil and condensate reserves, yet its production has consistently fallen short of its OPEC quota, and the government's target of 3 million barrels per day by 2030 assumes operational capacity the country has n…
  datacite Ezeagu, Vera Ijeoma 2026 置信度 0.66 upstream data infrastructuredata collaboration frameworkNigerian petroleum industryartificial intelligencemachine learning
• Locked Out of the Intelligence Revolution: Nigeria's Upstream Data Gap, the Billion-Dollar AI Opportunity, and a Technical Framework for Industry Data Collaboration in the Niger Delta
  Nigeria holds 37.01 billion barrels of proven crude oil and condensate reserves, yet its production has consistently fallen short of its OPEC quota, and the government's target of 3 million barrels per day by 2030 assumes operational capacity the country has n…
  datacite Ezeagu, Vera Ijeoma 2026 置信度 0.66 upstream data infrastructuredata collaboration frameworkNigerian petroleum industryartificial intelligencemachine learning
• Probabilistic Feature Imputation and Uncertainty-Aware Multimodal Federated Aggregation
  Multimodal federated learning enables privacy-preserving collaborative model training across healthcare institutions. However, a fundamental challenge arises from modality heterogeneity: many clinical sites possess only a subset of modalities due to resource c…
  datacite Shahid, Nafis Fuad, Ahmed, Maroof, Haider, Md Akib, Sagor, Saidur Rahman 等 2026 置信度 0.66 Image and Video Processing (eess.IV)Computer Vision and Pattern Recognition (cs.CV)FOS: Electrical engineering, electronic engineering, information engineeringFOS: Electrical engineering, electronic engineering, information engineeringFOS: Computer and information sciences
• A Robust Framework for Secure Cardiovascular Risk Prediction: An Architectural Case Study of Differentially Private Federated Learning
  Accurate cardiovascular risk prediction is crucial for preventive healthcare; however, the development of robust Artificial Intelligence (AI) models is hindered by the fragmentation of clinical data across institutions due to stringent privacy regulations. Thi…
  datacite Tertulino, Rodrigo, Alencar, Laércio 2026 置信度 0.66 Machine Learning (cs.LG)Artificial Intelligence (cs.AI)Cryptography and Security (cs.CR)FOS: Computer and information sciencesFOS: Computer and information sciences
• FedOT: Ownership Verification and Leakage Tracing via Watermarks for Federated LDMs
  Training Latent Diffusion Models (LDMs) within Federated Learning (FL) has attracted increasing attention due to its ability to combine the powerful generative capacity of LDMs with the privacy-preserving properties of FL. However, FL requires sharing the glob…
  datacite Cheng, Wenlong, Gan, Yuan, Xu, Yunqiu, Miao, Jiaxu 2026 置信度 0.66 Computer Vision and Pattern Recognition (cs.CV)FOS: Computer and information sciencesFOS: Computer and information sciences
• Federated Bilevel Performative Prediction
  Federated bilevel optimization is widely used for nested learning problems across distributed clients, such as federated hyperparameter tuning and meta-learning under privacy and communication constraints. Most existing formulations assume fixed client data di…
  datacite Qian, Liangxin, Liu, Chang, Cao, Xuanyu, Zhao, Jun 等 2026 置信度 0.66 Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• AI-Driven Speech Biomarkers for Parkinson's Disease: A Systematic Review of Explainable Machine Learning, Multimodal Fusion, and Longitudinal Validation
  This systematic literature review examines the current state of evidence for artificial intelligence (AI) and machine learning (ML) frameworks that utilize speech signals as digital biomarkers for Parkinson's disease (PD) diagnosis, progression tracking, and t…
  datacite Mohajeri, Najmeh 2026 置信度 0.66 Medicine and Health SciencesEngineering
• FinP: Fairness-in-Privacy in Federated Learning by Addressing Disparities in Privacy Risk
  Federated Learning (FL) inherently mitigates mass data centralization risks; however, its privacy protections are not equally distributed - leaving vulnerable individuals disproportionately exposed to sophisticated privacy attacks. Crucially, statistical heter…
  datacite Zhao, Tianyu, Srewa, Mahmoud, Elmalaki, Salma 2025 置信度 0.66 Machine Learning (cs.LG)Cryptography and Security (cs.CR)FOS: Computer and information sciencesFOS: 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
• CLoVE: Personalized Federated Learning through Clustering of Loss Vector Embeddings
  We propose CLoVE (Clustering of Loss Vector Embeddings), a novel algorithm for Clustered Federated Learning (CFL). In CFL, clients are naturally grouped into clusters based on their data distribution. However, identifying these clusters is challenging, as clie…
  datacite Bhatia, Randeep, Papadis, Nikos, Kodialam, Murali, Lakshman, TV 等 2025 置信度 0.66 Machine Learning (cs.LG)Artificial Intelligence (cs.AI)FOS: Computer and information sciencesFOS: Computer and information sciences
• Federated Foundation Language Model Post-Training Should Focus on Open-Source Models
  Post-training of foundation language models has emerged as a promising research domain in federated learning (FL) with the goal to enable privacy-preserving model improvements and adaptations to user's downstream tasks. Recent advances in this area adopt centr…
  datacite Agrawal, Nikita, Mayer, Ruben 2025 置信度 0.66 Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• Mosaic: Data-Free Knowledge Distillation via Mixture-of-Experts for Heterogeneous Distributed Environments
  Federated Learning (FL) is a decentralized machine learning paradigm that enables clients to collaboratively train models while preserving data privacy. However, the coexistence of model and data heterogeneity gives rise to inconsistent representations and div…
  datacite Liu, Junming, Gao, Yanting, Li, Yuqi, Meng, Siyuan 等 2025 置信度 0.66 Machine Learning (cs.LG)Artificial Intelligence (cs.AI)Distributed, Parallel, and Cluster Computing (cs.DC)FOS: Computer and information sciencesFOS: Computer and information sciences
• Distribution Alignment for One-Shot Federated Learning via Optimal Transport
  One-Shot Federated Learning (OSFL) addresses extreme communication regimes in which clients interact with the server only once, amplifying the impact of heterogeneous client data distributions. In particular, the interaction of domain shift and label shift acr…
  datacite Berardini, Daniele, Pastore, Vito Paolo, Murino, Vittorio 2026 置信度 0.66 Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• MUFFLe: Efficient Model Update Compression via Generalized Deduplication for Federated Learning
  Federated learning is well suited to edge environments but is often limited by the uplink cost of transmitting model updates. This Work-in-Progress paper presents MUFFLe, a communication-efficient update compression scheme that integrates generalized deduplica…
  datacite Zhao, Xiaobo, Lucani, Daniel E. 2026 置信度 0.66 Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• FedSPC: Shared Parameter Correction for Personalized Federated Learning
  Personalized federated learning (PFL) is one of the important approaches in federated learning for addressing statistical heterogeneity while enabling client-specific adaptation. Many PFL methods split the model into shared and personalized parameters, which a…
  datacite Menon, Kannanthodath Induchoodan Ajay, Prehofer, Christian, Xu, Yunfei, Hirano, Toru 2026 置信度 0.66 Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• The Latent Yield: A Formal Framework for Low-Inhibition Cognitive Workspace Architecture, Privacy-Preserving Multi-Modal Feature Projection, and the Mapping of Human Latent Capacity in Decentralized, Peer-to-Peer Sovereign Data Nodes
  This paper presents a theoretical framework and enabling specification for a low-inhibition associative cognitive workspace, designated the Dream Galaxy, implemented within decentralized, peer-to-peer sovereign data nodes constituting a federated personal cogn…
  datacite Hoppe, Eric 2026 置信度 0.66 ow-inhibition cognitive workspacepersistent homologyasynchronous local topological optimizationprivacy-preserving multi-modal feature projectionsub-threshold physiological tokenization
• 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-MRO: Maintenance Safety Pillar of the CDSA Federated Reinforcement Learning Paradigm — Multi-modal Engine + Cyber + Maintenance Fusion
  CDSA-MRO is the reference implementation of the maintenance safety 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,…
  datacite Cantekin, Mete 2026 置信度 0.66 reinforcement learningsynthetic datacyber safetyaviation maintenancedata locality
• FedBiCross: Personalized One-Shot Federated Learning on Medical Images
  Data-free knowledge distillation-based one-shot federated learning (OSFL) trains a model in a single communication round without sharing raw data, making OSFL attractive for privacy-sensitive medical applications. However, existing methods aggregate prediction…
  datacite Xia, Yuexuan, Zhang, Yinghao, Liu, Yalin, Dai, Hong-Ning 等 2026 置信度 0.66 Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• A Privacy-Preserving Framework Using Remote Data Science for Inter-Institutional Student Retention Prediction
  This study explores privacy-preserving machine learning (PPML) techniques using the PySyft platform to enable collaborative prediction of student retention between institutions. We developed a remote data science (RDS) framework with a semi-air-gapped architec…
  datacite Fields, John, Islam, K M Sajjadul, Thota, Ruchitha, Chen, Victor 等 2026 置信度 0.66 Cryptography and Security (cs.CR)Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• CDSA-ATM: Air Traffic Management Pillar of the CDSA Federated Reinforcement Learning Paradigm — Multi-modal Trajectory + ATS + ANSP Fusion
  CDSA-ATM is the reference implementation of the air traffic management pillar of the Complementary Diagnostic Safety Approach (CDSA), a third-generation aviation safety paradigm formalised as a Federated Reinforcement Learning (FRL) framework following the CDS…
  datacite Cantekin, Mete 2026 置信度 0.66 federated reinforcement learningair traffic managementADS-BOpenSky NetworkATS occurrences