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• Enhancing IoT Security and Trust Management Through Blockchain-Based Federated Learing Models
  crossref MUTHUPANDI G, Jayakumar K, Vignesh J 2026-05-03T20:52:58Z 置信度 0.70
• On the Suitability of Federated Learning Algorithms for Intrusion Detection
  crossref Filip Johnsson, Zeeshan Afzal, Mikael Asplund 2026-05-22T12:25:02Z 置信度 0.70
• Federated Learning for Diabetic Retinopathy: Enhancing Detection with Data Privacy
  crossref S.K. Susee, S. Sandhya, M. Senthil Kumar, B. Chidhambararajan 2026-05-08T21:41:08Z 置信度 0.70
• Introduction to Federated Learning for Smart Mobility
  crossref Jiaming Pei, Lukun Wang, Minghui Dai 2026-01-30T11:49:58Z 置信度 0.70
• Federated Robustness Analysis of Machine Learning Models for Colorectal Cancer Outcome Prediction
  crossref Fatemeh Akbarian, Narasimha Raghavan Veeraragavan, Jan F. Nygård, Amir Aminifar 2026-07-29T19:09:23Z 置信度 0.70
• Safeguarding Knowledge in Federated Transfer Learning with Direction-Aware DP
  crossref Yasas Supeksala Akurudda Liyanage Don, Thilina Ranbaduge, Ming Ding, Caslon Chua 等 2026-05-22T12:25:02Z 置信度 0.70
• Integrating Federated Learning with Satellite‐Based Geospatial Analysis for Urban Lake Management
  crossref Rohini Yadawar, Kh. Moirangleima, Shailendra Patni 2026-06-23T12:51:06Z 置信度 0.70
• FedSME: Spatial Mode Entropy Aware Aggregation for Federated Learning
  Sixth generation (6G) and Industrial IoT (IIoT) networks often operate in overload regimes. In these settings, the number of devices (Ktot) far exceeds the number of base station antennas (N), that is, Ktot ≫ N. Federated learning (FL) enables distributed trai…
  crossref Dhaarani Subramanian, Qilian Liang 2026-01-03T00:04:10Z 置信度 0.70
• Federated Learning for Grid-Edge Intelligence in Distributed Energy Systems
  <p><span>This review study comprehensively examines the role of the Federated Learning (FL) approach in the development of grid-edge intelligence within distributed energy systems. The digitalization of energy infrastructures and the increasing int…
  crossref Serhat Isikli 2026-07-29T13:10:03Z 置信度 0.70
• Federated Reinforcement Learning-based Energy Trading Optimization in Smart Microgrids
  The increasing penetration of distributed energy resources (DERs) and the decentralization of power grids necessitate robust, privacy-preserving energy trading mechanisms for smart microgrids. Traditional centralized optimization methods expose sensitive user …
  crossref Steve Albert 2026-06-19T06:57:49Z 置信度 0.70
• A Preliminary CoI-Based Federated Learning Platform for Artificial Intelligence of Things
  crossref Jesús Goterris, Alejandro Díaz-Rivero, Àngel Ruiz-Fas, Celia Sáenz-Martínez 等 2026-07-29T19:16:12Z 置信度 0.70
• Privacy-Preserving Data Processing and Federated Learning in Cloud Systems
  Cloud computing has transformed the way organizations store, process, and analyze data. However, the widespread adoption of cloud systems raises significant concerns regarding privacy, security, and compliance. This research investigates privacy-preserving dat…
  crossref Taylor Robert 2026-03-30T16:10:23Z 置信度 0.70
• Fundamentals of Federated Learning: Principles and Applications
  crossref Wasswa Shafik 2026-02-06T05:46:45Z 置信度 0.70
• Federated Learning and Trust Fabric for Cross-Domain Network Resilience
  crossref Omole Oreoluwa 2026-01-21T13:52:02Z 置信度 0.70
• Fed-CausalDiff: Decoupled Synchronization for Federated Do-Simulation and Policy Evaluation
  crossref Pengfei Li, Mohammad Khalil 2026-07-29T19:11:07Z 置信度 0.70
• Privacy Protection in FL
  crossref Alexander Jung 2026-01-02T02:23:17Z 置信度 0.70
• Federated Learning
  crossref Jeremiah D. Deng 2026-04-16T09:26:26Z 置信度 0.70
• A Contract Theory–Driven Adaptive Incentive Mechanism for Heterogeneous Federated Learning
  To address information asymmetry, weak behavioral constraints, and performance degradation under client heterogeneity in federated learning, this paper proposes a Contract Theory–Driven Adaptive Incentive Mechanism for Heterogeneous Federated Learning (CTAI-HF…
  crossref lin jia 2026-05-02T14:37:46Z 置信度 0.70
• Blockchain‐Integrated Federated Learning for Secure and Transparent Agricultural Supply Chains
  crossref M. Lakshmanan, A. Vegi Fernando, Mitha Guru, Sugandha Saxena 等 2026-06-23T12:51:06Z 置信度 0.70
• Federated Learning
  crossref Yiran Chen, Hai Li, Huanrui Yang 2026-05-23T11:14:03Z 置信度 0.70
• Federated Learning and Privacy-Preserving Artificial Intelligence for Distributed Healthcare Applications
  The proliferation of artificial intelligence in healthcare has been constrained by a fundamental tension: the need for large, diverse datasets to train robust clinical models versus the imperative to protect patient privacy and comply with stringent data prote…
  crossref Patrick Emmeson 2026-07-29T14:12:48Z 置信度 0.70
• Federated Learning for Spatio-Temporal Data
  crossref Hao Miao, Bin Yang 2026-07-21T04:29:09Z 置信度 0.70
• Farmer‐Centric Artificial Intelligence through Explainable Federated Learning for Smart Agriculture
  crossref R. Sriramkumar, Joshuva Arockia Dhanraj, M. Lakshmanan, A. Vegi Fernando 等 2026-06-23T12:51:06Z 置信度 0.70
• Blockchain based Homomorphic Encryption in Federated Learning for Alzheimer Detection
  Alzheimer disease is a progressive neuro degenerative condition that slowly damages the brain and affects memory and thinking. Early treatment is important to slow its progression and improve the patient's quality of life. Currently millions of people are suff…
  crossref Shashi Ranjan, Poonguzhali N 2026-02-25T19:53:48Z 置信度 0.70
• Federated learning as a collaborative learning algorithm in 6G
  crossref Abdelmadjid Benarfa, Muhammad Hassan Khan, Mohamed Bachir Yagoubi 2026-06-05T12:32:32Z 置信度 0.70
• SRFed: Mitigating Poisoning Attacks in Privacy-Preserving Federated Learning with Heterogeneous Data
  Federated Learning (FL) enables collaborative model training without exposing clients' private data, and has been widely adopted in privacy-sensitive scenarios. However, FL faces two critical security threats: curious servers that may launch inference attacks …
  crossref Yiwen Lu 2026-02-25T15:16:22Z 置信度 0.70
• A Design Principle for FL
  crossref Alexander Jung 2026-01-02T02:23:08Z 置信度 0.70
• CausalFedProto: Decoupling Federated Prototype Learning from a Causal Perspective
  Federated learning has demonstrated significant value in privacy protection and collaborative training. However, data heterogeneity (domain drift) causes models to overfit domain-specific noise, severely limiting their cross-domain generalization capabilities.…
  crossref Xingwen Fang, Fenhua Bai 2026-07-10T14:43:40Z 置信度 0.70
• Privacy-Preserving Lightweight Federated Learning Framework for Sepsis Prediction
  crossref Lahiruni Chamudika Kumari Pallewela 2026-01-26T16:01:28Z 置信度 0.70
• Weighted Over-the-Air Federated Learning
  This paper introduces a new federated learning scheme that leverages over-the-air computation. The novel feature of this scheme is the proposal to employ adaptive weights during aggregation, as opposed to predefined weights in existing over-the-air schemes. Th…
  crossref Seyed Mohammad Azimi-Abarghouyi, Leandros Tassiulas, Carlo Fischione 2026-02-27T14:09:01Z 置信度 0.70
• From Rules to Predictions: Federated Tabular Learning with LLM Reasoning
  crossref Afsaneh Mahanipour, Hana Khamfroush 2026-07-01T12:25:50Z 置信度 0.70
• A Novel Solving Algorithm for ELM Models in Online Sequential Learning and Federated Learning Scenarios
  Extreme learning machine (ELM), a type of single-hidden-layer feedforward neural networks (SLFNs), exhibits strong generalization and rapid learning. Enhancing the learning capacity of ELM is crucial for handling large datasets and online learning tasks. To su…
  crossref Zhihong Miao, Qing He 2026-05-02T15:43:31Z 置信度 0.70
• Quantum-Secured Federated and Lottery Federated Learning for Privacy-Preserving AI
  crossref Abirami B, Karthika Renuka D, Anusuya R 2026-03-27T19:48:10Z 置信度 0.70
• Evaluation of Split Federated Learning for 6G IoT Optimization: A Comparative Study with Baseline, Federated Learning, and Split Learning Models
  crossref Samar H. Alkayat, Ali H. Hamad 2026-07-03T05:59:11Z 置信度 0.70
• Next-gen and autonomous federated systems
  crossref K. S. Divya, K. Soumya 2026-03-25T15:39:21Z 置信度 0.70
• Split federated learning: an enhanced collaborative learning algorithm for ResourceLimited 6G contexts
  crossref Lakhdar Kamel Ouladdjedid, Mohammed Kamel Benhaoua 2026-06-05T12:32:32Z 置信度 0.70
• Value-Driven Federated Continual Learning Without Predefined Tasks
  crossref Zhigang Zhou, Hao Li, Li Yi 2026-01-12T16:42:39Z 置信度 0.70
• Future Trends in Federated Learning for Next-Generation Healthcare
  crossref Wasswa Shafik, Ssentumbwe Male Abdul 2026-02-06T05:46:49Z 置信度 0.70
• Privacy-Preserving Federated Learning for Feeder-Level Outage Prediction in Distribution Grids
  Here is the problem in one sentence: European and Middle Eastern power utilities hold exactly the data needed to predict grid failures, but sharing it between organisations is-in most jurisdictions-illegal. GDPR, energy market confidentiality rules, and Gulf d…
  crossref Zishan khan 2026-04-28T07:51:42Z 置信度 0.70
• Addressing Computational Overhead in Federated Learning Models in Healthcare 5.0 and Beyond
  crossref Wasswa Shafik 2026-02-06T05:46:52Z 置信度 0.70
• Cybersecurity in FL: Attacks and Defenses
  crossref Alexander Jung 2026-01-02T02:29:15Z 置信度 0.70
• Eigenstructure-Based Traceability Mechanisms for Federated Machine Learned Operators and Triggers
  crossref Keith R. Tinsley, Douglas R. Crocker, Wenzheng Sun 2026-07-29T19:12:20Z 置信度 0.70
• Exploring quantum federated learning
  crossref Nouhaila Innan, Alberto Marchisio, Mohamed Bennai, Muhammad Shafique 2025-09-13T16:13:30Z 置信度 0.70
• Federated learning and machine learning for the detection of heart diseases
  crossref Gaurav Kumar, Swati Sah, R. Shyam 2026-04-10T10:13:51Z 置信度 0.70
• Regulatory Frameworks: HIPAA, GDPR, and Compliance in Federated Learning
  crossref Wasswa Shafik, Mussa Saidi Abubakari 2026-02-06T05:46:49Z 置信度 0.70
• Applying federated population-based hyperparameter optimization to server aggregation methods
  crossref Claudia Großer, Mark Buckley, Denis Krompaß, Thomas A. Runkler 2026-07-29T19:09:28Z 置信度 0.70
• Relevance-aware parameter distillation for communication-efficient federated learning
  crossref Giovanni Paragliola 2026-06-12T23:56:27Z 置信度 0.70
• Virtual clinical and hospital in India
  crossref Himel Mondal, Shaikat Mondal 2025-09-12T12:25:54Z 置信度 0.70
• Privacy-preserving Energy Management in Microgrid Trading Networks using Federated Reinforcement Learning
  The integration of distributed energy resources (DERs) into microgrid trading networks introduces significant privacy and security challenges for traditional centralized energy management systems. This paper proposes a novel framework that combines federated l…
  crossref Steve Albert 2026-06-19T07:42:47Z 置信度 0.70
• FRLTC: A new topology control algorithm using federated reinforcement learning for software-defined wireless sensor network in IoT.
  Topology is one of the most important factors influencing the performance of software-defined wireless sensor networks (SDWSN) in the Internet of Things (IoT). This paper presents a novel topology control algorithm using federated reinforcement learning, propo…
  pubmed Binh LH, T Duong TV, Le DH 2026 置信度 0.82
• FedSwin-LHTP: Structure-Aware Hessian-Inspired Token Pruning for Efficient Federated Skin Lesion Classification.
  Background: Skin cancer encompasses a diverse range of malignancies and remains a significant global health challenge. Accurate machine-learning-assisted diagnosis can substantially improve patient outcomes through early detection and timely clinical intervent…
  pubmed Awais M, Junejo RH 2026 置信度 0.82
• Multicenter Privacy-Preserving Federated Models for Predicting Postoperative Delirium and Acute Kidney Injury in Older Patients.
  Postoperative delirium (POD) and acute kidney injury (AKI) are serious complications in older patients undergoing surgery, yet predictive model development is often constrained by single-center data limitations and privacy concerns that preclude centralized da…
  pubmed Wang Q, Song YX, Yang XD, Zhang JW 等 2026 置信度 0.82
• Federated Quantum Machine Learning over Satellite Networks: Toward Scalable Distributed Quantum Classification.
  Federated learning enables multiple organizations to collaboratively analyze data while retaining local control over their datasets, making it attractive for applications such as healthcare. Distributed quantum computing provides a natural framework for such w…
  pubmed Boschero JC, Oancea RA, Mazzarella L, Doeleman H 等 2026 置信度 0.82
• A Federated Machine Learning Approach for the Detection and Visualisation of Eye Diseases Using Activation Maps.
  Eye diseases, particularly glaucoma and cataracts, are the leading causes of visual impairment, compromising quality of life. Automatic classification of these diseases can lead to more accurate and timely diagnosis, thereby limiting their progression and comp…
  pubmed Niro F, Renzo MD, Agnello P, Petyx M 等 2026 置信度 0.82
• Seizure detection using hierarchical temporal trend integration and self-supervised learning.
  Early, reliable detection of epileptic seizures from electroencephalography (EEG) remains challenging due to label scarcity, inter patient variability, and the non stationary nature of clinical recordings. This work introduces Federated Temporal Learning (FTL)…
  pubmed R NA, K SB 2026 置信度 0.82
• Federated Hybrid Deep Learning Framework for IoT Intrusion Detection Using N-BaIoT, CICIoT2023, and BoT-IoT Datasets
  Abstract Internet of Things (IoT) environments expose many heterogeneous and resource-constrained devices to botnet, distributed denial-of-service, denial-of-service, reconnaissance, spoofing, malware, and service-scanning attacks. Centralized intrusion detect…
  europepmc Sonali Mishra^, R. Venkata Siva 2026 置信度 0.80
• Dual-Layer PSO-Enhanced Federated Heterogeneous Data Fusion for Hemodialysis Complication Prediction.
  Taiwan has one of the highest dialysis prevalences worldwide, making safe and reliable hemodialysis monitoring a critical sensor-based healthcare challenge. Modern hemodialysis machines integrate heterogeneous multimodal sensors (pressure, flow, conductivity, …
  pubmed Shih C, Chen CH, Yeah X 2026 置信度 0.82
• A Federated Graph-Based Intrusion Detection Framework for Unknown Attack Detection in Internet of Medical Things(IoMT)
  europepmc 2026 置信度 0.80
• FedAD: adaptive federated disentanglement for domain generalization in unlabeled medical imaging.
  Three barriers significantly hinder the use of deep learning in medical imaging: poor generalization to new clinical domain shifts, label scarcity, and data privacy. A unified framework that learns from unlabeled, decentralized data while optimizing for genera…
  pubmed Kodhandaraman S, K H, Prabhakar A, Kempapuram S 2026 置信度 0.82
• SFLFEM: A frequency-enhanced mamba framework for selective personalized federated breast ultrasound diagnosis.
  Breast ultrasound is essential for lesion assessment and early cancer screening. However, deploying robust deep learning models in clinical practice faces two primary challenges. The first is intrinsic: lesions often exhibit complex textures, speckle-corrupted…
  pubmed He X, Xu X, Xiao F, Ma J 等 2026 置信度 0.82
• Real-time PEM fuel cell fault classification in smart manufacturing plant by using uncertainty aware federated deep learning.
  The Proton Exchange Membrane Fuel Cells (PEMFCs) is not the least significant component of the modern industrial, yet the reliable operation of the smart manufacturing environment cannot be guaranteed since the breakdowns of PEMFCs are multiple and highly comp…
  pubmed Aurangzeb M, Shusheng X, Iqbal S, Shafiullah M 等 2026 置信度 0.82
• Federated spatio-temporal graph neural network for privacy-preserving vehicle trajectory prediction in autonomous driving.
  Accurate vehicle trajectory prediction plays a vital role in autonomous driving and intelligent transport systems. Deep learning models like LSTM, CNN, GNN, etc., have shown remarkable performance but often operate in a centralized setting, aggregating raw tra…
  pubmed Joshi A, P A, Prajapati V, Anbalagan S 等 2026 置信度 0.82
• Smart FL: meta-learning for federated blood marrow smear classification.
  Accurate classification of bone marrow cells is crucial for diagnosing hematological disorders, yet integrating machine learning and deep learning models into clinical practice faces hurdles like data privacy and data scarcity. Existing models often lack the a…
  pubmed Ilakiyaselvan N, Sanskar S, Aarthi D, Kalyanasundaram V 2026 置信度 0.82
• A Linearized Alternating Direction Multiplier Method for Federated Matrix Completion Problems.
  europepmc 2026 置信度 0.80
• Privacy-preserving adaptive arrhythmia monitoring with wearable ECG, smartphone PPG, and federated on-device learning.
  Wearable electrocardiography (ECG) and smartphone technology create new opportunities for arrhythmia monitoring outside clinical environments. However, deployable mobile cardiac systems must address more than just classification accuracy. They must reliably ac…
  pubmed Kartali A, Koshtrevska M, Jokić S, Gligorić N 等 2026 置信度 0.82
• EquiFL-X: Communication-constrained federated calibration via label-aggregated histograms for multi-view screening mammography under acquisition shift.
  Reliable probability estimates and robust performance across heterogeneous acquisition settings are important for artificial intelligence-assisted screening mammography. In federated learning, acquisition-driven non-identically distributed data can impair both…
  pubmed Öney B 2026 置信度 0.82
• APEW-Fed: adaptive privacy-aware ensemble weighting for federated anomaly detection in enterprise resource planning systems.
  Enterprise Resource Planning (ERP) systems are high-value targets for fraud owing to the sensitive financial and operational data they process. Anomaly detection in such systems faces a fundamental tension: centralising data for accurate detection conflic…
  pubmed Qazi A, Ali Khan Jadoon A, Arshad S 2026 置信度 0.82
• Federated Scientific Machine Learning Framework for Multi-Scale Inverse Parameter Identification: Application to Nonlocal Strain Gradient Plasticity
  europepmc 2026 置信度 0.80
• Integrated design of an efficient multi spectral imaging and federated learning framework for precision crop disease diagnosis in low-resource farming communities.
  Crop diseases pose significant challenges to productivity in resource-constrained settings, often remaining undiagnosed when diagnostic tools and infrastructure are either non-existent or inadequate. Current crop disease diagnosis relies on manual inspection m…
  pubmed Sastry JSVRS, Naresh P, Ayesha A, Sardar TH 等 2026 置信度 0.82
• Federated causal discovery in medicine: trends, opportunities, and challenges.
  Exponential growth and continued digitisation have accelerated the adoption of data-driven and evidence-based approaches in medicine. This includes deciphering associations, including potential causal associations, from multivariate observational biomedical da…
  pubmed Rocchi N, Scutari M, Zanga A, Nagarajan R 等 2026 置信度 0.82
• Predicting asthma-related hospitalizations in underserved and rural populations in Algeria.
  We developed a comprehensive system architecture that incorporates a predictive module for assessing hospitalization risk due to asthma exacerbations. This module is specifically designed to promote equitable access to healthcare services for underserved popul…
  pubmed Bouamar A, Belalem G, Belaid B, Benatta D 等 2026 置信度 0.82
• Mobility-Aware Federated NOMA User Pairing with DBSCAN Clustering for Energy-Efficient IoT-Enabled VANETs
  europepmc 2026 置信度 0.80
• Clifti-GPT: privacy-preserving federated fine-tuning and transferable inference of foundation models on clinical single-cell data.
  Foundation models have demonstrated immense value for scRNA-seq analysis, but their fine-tuning or inference on heterogeneous, privacy-sensitive clinical cohorts is governed by strict data protection policies, which often prohibit centralization. We introduce …
  pubmed Bakhtiari M, Elkjaer ML, Can AO, Theis F 等 2026 置信度 0.82
• Communication-Efficient Federated Class-Incremental Intrusion Detection for Edge IoT Networks.
  The continuous emergence of new attack classes challenges intrusion detection in edge Internet of Things (IoT) networks. Although federated learning enables distributed devices to collaboratively train a shared detector without exchanging raw traffic data, mos…
  pubmed Wu Z, He B, Si Z, Qiu C 等 2026 置信度 0.82
• Defense Against Information Integrity Attacks in Federated IoT Systems Using Inertial Momentum Aware IALM-RPCA
  europepmc 2026 置信度 0.80
• A sustainable platform for federated health data access, AI innovation, and regulatory acceptance in alignment with the European Health Data Space principles
  The IDERHA ( I ntegration of Heterogeneous D ata and E vidence towards R egulatory and H TA A cceptance) project aims to enhance medical research by establishing one of Europe’s first pan-European, disease-agnostic health data spaces. Aligned with the European…
  europepmc 2026 置信度 0.80
• CCEO-DCABNet: Chronological Chaotic Evolution Optimization-Enabled Hybrid Deep Learning for Multiclass Disease Classification Using Chest X-Ray Images in Federated Learning.
  Background: Chest X-ray imaging is a widely used diagnostic modality for identifying various lung diseases. Accurate multiclass classification of lung diseases enables timely treatment and improves patient survival. However, disease detection using chest X-ray…
  pubmed Patil L, Garg B, Donelli M, Jain A 2026 置信度 0.82
• FedHyperM: Modality-Sensitive Federated Hypergraph Learning for Heterogeneous Multimodal Edge Intelligence
  europepmc 2026 置信度 0.80
• Privacy-Aware and Resource-Efficient Split Learning for IoT Botnet Detection: A Multi-Dataset Experimental and Systems Evaluation
  europepmc 2026 置信度 0.80
• Artificial Intelligence-Based Intrusion Detection Systems for Smart Home Security
  europepmc 2026 置信度 0.80
• CardioSafeAI: An Explainable Federated Machine Learning Framework with Multi-Stage Hybrid Feature Selection for Cardiovascular Disease Prediction
  cardiovascular disease (CVD) is a leading cause of global mortality and may progress without obvious symptoms. This study proposes CardioSafeAI, an explainable federated learning framework for predicting ten-year coronary heart disease risk from a public datas…
  europepmc 2026 置信度 0.80
• Interpretable-Aware Privacy Preserving Framework for Deepfake Detection using Federated ResNet and Explainable AI Techniques
  europepmc 2026 置信度 0.80
• A federated policy-governed architecture for sovereign European data spaces
  europepmc 2026 置信度 0.80
• HarmoFGL: Harmonizing GNN Latent Factors for Federated Graph Learning.
  europepmc 2026 置信度 0.80
• Secure Discovery of Genetic Relatives across Large-Scale and Distributed Genomic Datasets.
  europepmc 2024 置信度 0.80
• Challenges and future directions of AIRR-seq-based diagnostics.
  europepmc 2025 置信度 0.80
• Horizontal federated learning and assessment of Cox models.
  europepmc 2025 置信度 0.80
• Blockchain-Based Trusted Data Management with Privacy Preservation for Secure IoT Systems.
  europepmc 2025 置信度 0.80
• Experimental quantum Byzantine agreement on a three-user quantum network with integrated photonics.
  europepmc 2024 置信度 0.80
• Privacy-preserving techniques for decentralized and secure machine learning in drug discovery.
  Data availability, data security, and privacy concerns often hamper optimal performance efficiency of machine learning (ML) techniques. Therefore, novel techniques for the utilization of private/sensitive data in the field of drug discovery have been proposed …
  pubmed Smajić A, Grandits M, Ecker GF 2023 置信度 0.82
• Secure discovery of genetic relatives across large-scale and distributed genomic data sets.
  europepmc 2024 置信度 0.80
• The sequence alignment problem: boundary conditions as the unifying principle.
  europepmc 2026 置信度 0.80
• Lightweight Privacy-Preserving (t, m, n) Multi-Party Computation Based on Secret Sharing in Untrusted Cloud Environments
  europepmc 2023 置信度 0.80
• Blockchain Smart Contracts for Automating Clinical Trials: Systematic Review and Proposed System Architecture.
  europepmc 2026 置信度 0.80
• Ensuring Trust in Genomics Research.
  europepmc 2023 置信度 0.80
• Lightweight XOR-based visual cryptography using random shares for secure colour image sharing with minimal shares.
  europepmc 2025 置信度 0.80
• Identity-based linear homomorphic signature for a restricted combiners' group for e-commerce.
  europepmc 2025 置信度 0.80
• A Collaborative Data Sharing Platform to Accelerate Translation of Biomedical Innovations.
  europepmc 2025 置信度 0.80
• A Privacy-Preserving Framework Using Homomorphic Encryption for Smart Metering Systems.
  Smart metering systems (SMSs) have been widely used by industrial users and residential customers for purposes such as real-time tracking, outage notification, quality monitoring, load forecasting, etc. However, the consumption data it generates can violate cu…
  pubmed Xu W, Sun J, Cardell-Oliver R, Mian A 等 2023 置信度 0.82
• Secure and scalable gene expression quantification with pQuant.
  europepmc 2025 置信度 0.80