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• Mathematical and Algorithmic Advances in Machine Learning for Statistical Process Control: A Systematic Review.
  pubmed Qiao Y, Han T, Wu Z, Jin G 等 2026 Jan 29 置信度 0.82
• A Systematic Review of Contrastive Learning in Medical AI: Foundations, Biomedical Modalities, and Future Directions.
  pubmed Obaido G, Mienye ID, Aruleba K, Chukwu CW 等 2026 Feb 2 置信度 0.82
• Multimodal artificial intelligence for enhanced skin cancer diagnosis and prognosis.
  pubmed Karnwal A, Selvaraj M, Kumar G, Kumar A 等 2026 Feb 25 置信度 0.82
• Data mining in pediatric radiology in the era of artificial intelligence.
  pubmed Guarnera A, Ghosh A, Gikera R, Vahdati S 等 2026 Apr 置信度 0.82
• Artificial intelligence in paediatric neuroradiology: current landscape, challenges, and future directions.
  pubmed Kelly BS, Clifford SM, Mankad K, Colleran GC 2026 Feb 23 置信度 0.82
• Generative Adversarial Networks for Intrusion Detection Systems: A Comprehensive Survey of Applications, Challenges, and Research Directions.
  pubmed Alauthman M, Aslam N, Al-Qerem A, Aldweesh A 等 2026 置信度 0.82
• Ethical and legal challenges of integrating artificial intelligence into paediatric surgery.
  pubmed Anand A, Sinha CK 2026 Feb 23 置信度 0.82
• Overcoming domain-specific challenges for artificial intelligence in abdominal oncology toward clinical translation.
  pubmed Duan B, Bai X, Wang T, Wang T 等 2026 Feb 22 置信度 0.82
• Improving machine-learning development in allergology: bridging the gap between open-access and cohort-based databases.
  pubmed Arnau-Soler A, Corriger J, Chantran Y, Goret J 2026 Apr 1 置信度 0.82
• Artificial intelligence and machine learning applications in dialysis: Current applications, challenges, and future directions.
  pubmed Clement David-Olawade A, Ogunbona MA, Olawuyi OF, Makanjuola BD 等 2026 Apr 15 置信度 0.82
• Brugada syndrome risk scores: what we've learned and what's next.
  pubmed Rattanawong P, Shen WK 2025 置信度 0.82
• Pioneering efficient deep learning architectures for enhanced hepatocellular carcinoma prediction and clinical translation.
  pubmed Akbulut S, Colak C 2026 Feb 15 置信度 0.82
• Integrating artificial intelligence with genome sequencing against antimicrobial resistance: a narrative review.
  pubmed Scaglione G, Mastroianni N, Rizzo A, Palomba E 等 2026 置信度 0.82
• Computational approaches to multimodal data integration in rheumatoid arthritis: from data landscape to clinical translation.
  pubmed Xu D, Tang Y, Luo J, Wen C 2026 Jan 7 置信度 0.82
• Application of knowledge graphs in rare disease research.
  pubmed Fei Y, Ding H, Tong S, He Y 等 2026 置信度 0.82
• Power Control in Wireless Body Area Networks: A Review of Mechanisms, Challenges, and Future Directions.
  pubmed Su H, Zhao Z, Gu B, Lin S 2026 置信度 0.82
• Foundation models in radiology: a primer for pediatric radiologists.
  pubmed Gupta A, Fanni SC, Veiga-Canuto D, Guarnera A 2026 Feb 12 置信度 0.82
• Artificial Intelligence in Cerebrovascular Imaging: A Targeted Review of Aneurysm Detection and Rupture Risk Prediction.
  pubmed Le D 2026 Jan 15 置信度 0.82
• Spatiotemporal Graph Learning on Urban Environments.
  pubmed Li H, Hou L, Cui J, Wang Y 等 2026 Feb 24 置信度 0.82
• Risk prediction in IgA nephropathy: from conventional models to machine learning, deep learning, and precision nephrology.
  pubmed Xu H, Ge S 2026 Dec 置信度 0.82
• Performance of machine learning algorithms in diffusion tensor imaging of movement disorders: an exploratory meta-analysis.
  pubmed Fathollahi MA, Khani Y, Bayati H, Zaman S 等 2026 Feb 7 置信度 0.82
• DermaGPT a federated multimodal framework with a meta learned trust function for interpretable dermatology diagnostics.
  pubmed Hashjin NM, Amiri MH, Najafabadi MK 2026 Feb 7 置信度 0.82
• Digital Biomarkers for Precision Early Detection of Lung Cancer: Integrating AI-Driven Multi-Omics Into Clinical Pathways.
  pubmed Bu F, Ling ZQ 2026 Feb 置信度 0.82
• Sharing approaches in predictive genomics across animals, plants and humans.
  pubmed Arirangan S, de Oliveira LF, Hasan MN, B Sherman A 等 2026 Mar 置信度 0.82
• AI-driven routing and layered architectures for intelligent ICT in nanosensor networked systems.
  pubmed Yousif Dafhalla AK, Attia Gasmalla TA, Filali A, Osman Sid Ahmed NM 等 2026 Feb 20 置信度 0.82
• Federated multimodal AI for precision-equitable diabetes care.
  pubmed Bai B, Liu X, Li H 2025 置信度 0.82
• Artificial intelligence for colposcopic and cytological image analysis in early cervical cancer detection.
  pubmed Wang X, Wang Q, Ding G, Wang J 等 2026 Feb 20 置信度 0.82
• Advances and challenges in single-cell RNA sequencing data analysis: a comprehensive review.
  pubmed Nesari AM, MotieGhader H, Ghorbian S 2026 Jan 7 置信度 0.82
• Artificial intelligence in biobanking: current situation and future perspectives.
  pubmed Kinkorová J 2025 置信度 0.82
• Review for "Protecting health monitoring privacy in fitness training: A federated learning framework based on personalized differential privacy"
  crossref 2024-05-01T09:59:27Z 置信度 0.70
• Decentralized Federated Learning over Wireless Channels: A Review
  crossref Koya SATO 2022-06-30T22:18:28Z 置信度 0.70
• Advancements in Machine Learning and Deep Learning for Breast Cancer Detection: A Systematic Review
  Breast cancer is a significant transnational health concern, requiring effective timely detection methods to improve patient’s treatment result and reduce mortality rates. While conventional screening methods like mammography, ultrasound, and MRI have proven e…
  crossref Zeba Khan, Madhavidevi Botlagunta, Gorli L. Aruna Kumari, Pranjali Malviya 等 2024-12-20T17:50:37Z 置信度 0.70
• Private SVM Inference on Encrypted Data
  This tutorial chapter provides a comprehensive guide to implementing privacy-preserving Support Vector Machine (SVM) inference using Fully Homomorphic Encryption (FHE). We demonstrate a practical solution for secure and private SVM inference on encrypted data,…
  crossref Ahmad Al Badawi 2024-09-04T14:18:59Z 置信度 0.70
• Peer Review #2 of "SFedChain: blockchain-based federated learning scheme for secure data sharing in distributed energy storage networks (v0.1)"
  crossref 2022-07-04T02:31:31Z 置信度 0.70
• ProxyFL: Decentralized Federated Learning through Proxy Model Sharing
  Abstract Institutions in highly regulated domains such as finance and healthcare often have restrictive rules around data sharing. Federated learning is a distributed learning framework that enables multi-institutional collaborations on decentralized data with…
  crossref Shivam Kalra, Junfeng Wen, Jesse Cresswell, Maksims Volkovs 等 2021-12-16T13:41:18Z 置信度 0.70
• Review for "Protecting health monitoring privacy in fitness training: A federated learning framework based on personalized differential privacy"
  crossref 2024-05-01T09:59:27Z 置信度 0.70
• Review for "Protecting health monitoring privacy in fitness training: A federated learning framework based on personalized differential privacy"
  crossref 2024-05-01T09:59:27Z 置信度 0.70
• Survivable SFC deployment method based on federated learning in multi-domain networks
  Abstract In the multi-domain network scenario, in order to improve the survivability of service function chain in the face of network failure, most methods solve this problem through virtual network function backup mechanism. However, the traditional multi-dom…
  crossref Hua Qu, Ke Wang, Jihong Zhao 2022-10-05T20:48:53Z 置信度 0.70
• Review for "Protecting health monitoring privacy in fitness training: A federated learning framework based on personalized differential privacy"
  crossref 2024-05-01T09:59:27Z 置信度 0.70
• Review for "Protecting health monitoring privacy in fitness training: A federated learning framework based on personalized differential privacy"
  crossref 2024-05-01T09:59:27Z 置信度 0.70
• Privacy-Preserving Federated Learning in Medical AI: A Systematic Review of Techniques, Challenges, and the Clinical Deployment Gap
  crossref Chuanli Wei, Haoyang Guan 2026-01-07T18:29:53Z 置信度 0.70
• Federated Learning based Spatio-Temporal framework for real-time traffic prediction
  Abstract Wireless Sensor Network (WSN)is widely explored for traffic flow prediction. Traffic forecasting is a spatio-temporal problem because of the dynamic nature of road traffic. The data collected from users for traffic prediction is often private in natur…
  crossref Gaganbir Kaur, Surender K Grewal, Aarti Jain 2023-01-24T05:20:41Z 置信度 0.70
• Peer Review #2 of "SFedChain: blockchain-based federated learning scheme for secure data sharing in distributed energy storage networks (v0.2)"
  crossref 2022-07-04T02:31:38Z 置信度 0.70
• Peer Review #1 of "SFedChain: blockchain-based federated learning scheme for secure data sharing in distributed energy storage networks (v0.1)"
  crossref 2022-07-04T02:31:37Z 置信度 0.70
• Federated Learning for Ophthalmic Imaging: A Systematic Review
  Ophthalmic diseases are leading causes of vision loss worldwide, and medical imaging-based AI models are increasingly used to support their detection and management. However, the development of robust and generalisable models is hindered by siloed imaging data…
  crossref Luyuan Qi, Paul Taylor, Cathy Egan, Adnan Tufail 等 2026-02-25T15:17:36Z 置信度 0.70
• A Traffic Flow Prediction Method Based on Blockchain and Federated Learning
  Abstract Traffic flow prediction is the an important issue in the field of intelligent transportation, and real-time and accurate traffic flow prediction plays a crucial role in improving the efficiency of traffic networks. Existing traffic flow prediction met…
  crossref Hui Zhi, 苗苗 段, Lixia Yang 2023-10-12T02:37:58Z 置信度 0.70
• Federated Learning - A Systematic Review
  crossref 2024-12-06T14:09:44Z 置信度 0.70
• Federated learning-based detection and control mechanism of in-car navigation safety system
  Abstract The advancement of in-car navigation systems has dramatically improved driving experiences. However, ensuring the safety of these systems remains a critical concern. Federated learning provides a new solution for cooperative learning between non-mutua…
  crossref Jingge Gao, Shuqiang Zhang, Wei Lu 2023-07-18T15:29:20Z 置信度 0.70
• Defending against Label-Flipping Attacks in Federated Learning Systems with UMAP
  Abstract The importance of computing and storage capacity has increased over time, and the importance of data mining in industrial engineering has become more apparent. Recently, artificial intelligence and machine learning have made significant advancements i…
  crossref Deepak Upreti, Hyunil Kim, Eunmok Yang, Changho Seo 2022-08-25T17:34:59Z 置信度 0.70
• Peer Review #1 of "OES-Fed: a federated learning framework in vehicular network based on noise data filtering (v0.1)"
  crossref 2022-09-25T02:33:02Z 置信度 0.70
• Analysis of the Factors Influencing the Predictive Learning Performance Using Federated Learning
  Abstract Numerous educational institutions utilize data mining techniques to manage student records, particularly those related to academic achievements , which are essential in improving learning experiences and overall 1 Springer Nature 2021 L A T E X templa…
  crossref Umer Farooq, Shahid Naseem, Jianqiang Li, Tariq Mahmood 等 2023-08-14T02:15:40Z 置信度 0.70
• Federated Learning for Smart City Network Attack Detection and Classification: A Literature Review
  The growth of smart cities and the Internet of Things (IoT) has generated massive volumes of sensitive data, creating significant cybersecurity challenges and limitations for centralized machine learning models. Federated Learning (FL) has emerged as a decentr…
  crossref Kurniawan D. Irianto, Rian Nur Ikhsan, Fayruz Rahma 2026-03-13T15:08:22Z 置信度 0.70
• A Comparative Study of Federated Learning Methods for COVID-19 Detection
  Abstract Deep learning has proven to be highly effective in diagnosing COVID-19; however, its efficacy is contingent upon the availability of extensive data for model training. The data sharing among hospitals, which is crucial for training robust models, is o…
  crossref Erfan Darzi, Nanna M. Sijtsema, P.M.A van Ooijen 2023-09-12T18:51:37Z 置信度 0.70
• Stability-Aware and Feasibility-Sensitive Aggregation in Federated Reinforcement Learning for Edge-IoT Systems: A Review
  Federated Reinforcement Learning (FRL) provides a useful basis for distributed policy learning in Edge-IoT systems, where clients interact with local environments without transferring raw operational data to a central server. Yet aggregation becomes difficult …
  crossref Majid A. Aslan, Ahmed A. S. Al-shalabi, Ahmed S. Al-Hegami 2026-08-28T14:10:11Z 置信度 0.70
• Adaptive Confidence-Weighted Policy Aggregation: A Novel Method for Federated Reinforcement Learning
  Abstract This paper proposes an innovative Federated Reinforcement Learning (FRL) approach called the Adaptive Confidence-Weighted Policy Aggregation method, or ACWPA in short. In light of incomplete information and heterogeneous knowledge, ACWPA was developed…
  crossref Nematollah Ab Azar, Aref Shahmansoorian, Mohsen Davoudi 2025-05-21T01:29:39Z 置信度 0.70
• Blockchain-enabled defenses in federated learning: A comprehensive survey
  crossref Omar Dib 2026-02-19T11:17:41Z 置信度 0.70
• A Systematic Review of Federated Structure Learning: Definitions and Methods
  The integration of causal reasoning into machine learning addresses fundamental limitations in predictive modeling, yet deploying these methods across distributed, privacy constrained datasets remains a critical bottleneck. Federated Structure Learning (FSL), …
  crossref Alvaro Javier Vargas Guerrero, Arnau Dillen, Johan Loeckx, Guy Nagels 2026-07-24T08:58:56Z 置信度 0.70
• Enhancing Communication and Comprehension for Individuals with Special Needs through Federated Learning: A Deep Learning Approach
  Abstract Individuals with special needs most of the time find it harder to identify hazards and dangers as well as circumstances that are socially challenging. Hence, they face the risk of falling victim to abuse and violence. In this paper, the main goal is t…
  crossref Tharwat Elsayed, Mohamed Elrashidy, Ayman EL-Sayed, Abdullah N. Moustafa 2023-10-11T16:34:46Z 置信度 0.70
• Privacy-Preserving Data Analysis Using Federated Learning: A Practical Implementation Study
  crossref Wenkun Ren, Juan Li, Xiaolan Wu 2026-01-07T18:29:58Z 置信度 0.70
• Recognizing Heat Exchange Crisis Using the SVM ALG
  This article discusses the use of support vector machines to recognize a heat transfer crisis. A heat transfer crisis during boiling is the phenomenon of a sharp deterioration in heat transfer on a heat transfer surface, leading, as a rule, to a rapid increase…
  crossref Viktoria Sheshukova 2025-04-02T13:31:30Z 置信度 0.70
• Research Review on Federated Learning Technology for Fault Diagnosis
  Federated learning (FL) is a distributed machine learning (ML) method. This technology only needs to exchange model parameters without sharing private data and plays an important role in industrial fault diagnosis. This paper focuses on analysing four mainstre…
  crossref Xuebin Han 2026-04-14T04:08:42Z 置信度 0.70
• Blockchain-Governed Federated Intelligence: A Formal Model for Trust-Aware and Auditable Collaborative Learning
  Abstract The joint artificial intelligence across cross-organizational digital ecosystems requires coordination strategies to maintain reliability where one of the organizations has no central, trusted controller. Although federated learning requires minimal e…
  crossref Hossein Hosseinalibeiki, Reza Sepehrzad, Majid Heidari 2026-02-10T11:21:34Z 置信度 0.70
• Privacy-Preserving Federated Learning Based On Partial Low-Quality Data
  Abstract Traditional machine learning requires collecting data from participants for training, which may result in malicious acquisition of privacy in participants' data. Federated learning offers a method to protect participants' data privacy by transferring …
  crossref Huiyong Wang, Qi Wang, Yong Ding, Shijie Tang 等 2023-10-12T04:14:15Z 置信度 0.70
• Balancing Privacy and Explainability in Federated Learning
  Abstract As we advance towards future generations of communication networks, such as 6G, Artificial Intelligence (AI) and Machine Learning (ML) will assume an increasingly pivotal role in network optimization, management, and operation. In this context, the pu…
  crossref Rafael Teixeira, Leonardo Almeida, Pedro Rodrigues, Julio Corona 等 2023-12-08T06:16:51Z 置信度 0.70
• Federated Learning in Smart Healthcare, Homes, and Cities (FL-SHHC): A Systematic Literature Review
  Federated learning (FL) has emerged as an acceptable approach for training machine learning (ML) models across distributed devices while preserving their private data. Recently, Internet of Things (IoTs) have been widely used across three user privacy domains:…
  crossref Tom Jackson, Javed Ali Khan, Alexios Mylonas 2026-06-17T00:42:08Z 置信度 0.70
• Federated Learning for Acute Lymphoblastic Leukemia Subtype Classification: A Systematic Review of Privacy-Preserving Artificial Intelligence in Hematologic Oncology
  Acute Lymphoblastic Leukemia (ALL) remains one of the most clinically significant hematologic malignancies, requiring accurate subtype classification to support diagnosis, risk stratification, and treatment planning. Recent advances in artificial intelligence …
  crossref Aremu Feranmi 2026-07-29T13:49:49Z 置信度 0.70
• Efficiency-optimized Data Perturbation in Local Differentially Private Federated Learning
  Abstract Federated learning (FL) pours vitality into developing data-driven AI. However, there are still some challenges, such as balancing the security and efficiency in FL. Differential privacy is one of the dominant means in privacy-preserving machine learn…
  crossref Jianzhe Zhao, Mengbo Yang, Jiali Zheng, Jingran Feng 等 2022-08-01T14:37:09Z 置信度 0.70
• An Efficient IoT DataStream Prediction using Integrated Federated Learning with CRSO of Attention-based LSTM Framework
  Abstract Real-time data stream processing presents a significant challenge in the rapidly changing Internet of Things (IoT) environment. Traditional centralized approaches face hurdles in handling the high velocity and volume of IoT data, especially in real-ti…
  crossref Asma M. El-Saied 2023-11-06T16:36:18Z 置信度 0.70
• An Integrated Federated Learning with CRSO of Attention-based LSTM Framework for Efficient IoT DataStream Prediction
  Abstract Real-time data stream processing presents a significant challenge in the rapidly changing Internet of Things (IoT) environment. Traditional centralized approaches face hurdles in handling the high velocity and volume of IoT data, especially in real-ti…
  crossref Asma M. El-Saied 2023-11-07T01:17:38Z 置信度 0.70
• FedRCD: A Federated Learning Algorithm Leveraging Distribution Extraction and Community Detection for Enhanced Clustering
  Abstract Clustering clients and performing federated learning within clusters is an effective method for overcoming the constraint of traditional federated learning algorithms in non-IID data scenarios. However, existing methods often rely on iterative process…
  crossref Ruicong Wang, Naizheng Bian, Yingjun Wu 2023-07-28T06:00:25Z 置信度 0.70
• Enhanced Decentralized Federated Learning based on Consensus in Connected Vehicles
  Abstract Advanced researches on connected vehicles have recently targeted to the integration of vehicle-to-everything (V2X) networks with Machine Learning (ML) tools and distributed decision making. Federated learning (FL) is emerging as a new paradigm to trai…
  crossref Xiaoyan Liu, Zehui Dong, Zhiwei Xu, Siyuan Liu 等 2023-03-24T02:55:11Z 置信度 0.70
• Privacy-Preserving Local Energy Trading Considering Network Fees
  Driven by the widespread deployment of distributed energy resources, local energy markets (LEMs) have emerged as a promising approach for enabling direct trades among prosumers and consumers to balance intermittent generation and demand locally. However, LEMs …
  datacite Alqahtani, Eman, Mustafa, Mustafa A. 2026 置信度 0.66 Cryptography and Security (cs.CR)FOS: Computer and information sciencesFOS: Computer and information sciences
• Additional file 1 of EasySMPC: a simple but powerful no-code tool for practical secure multiparty computation
  Additional file 1. Microsoft Word format describes the employed SMPC method in detail.
  datacite Wirth, Felix Nikolaus, Kussel, Tobias, Müller, Armin, Hamacher, Kay 等 2024 置信度 0.66 Computation Theory and MathematicsFOS: Computer and information sciencesComputer SoftwareData FormatInformation Systems
• Additional file 1 of EasySMPC: a simple but powerful no-code tool for practical secure multiparty computation
  Additional file 1. Microsoft Word format describes the employed SMPC method in detail.
  datacite Wirth, Felix Nikolaus, Kussel, Tobias, Müller, Armin, Hamacher, Kay 等 2024 置信度 0.66 Computation Theory and MathematicsFOS: Computer and information sciencesComputer SoftwareData FormatInformation Systems
• Additional file 2 of EasySMPC: a simple but powerful no-code tool for practical secure multiparty computation
  Additional file 2. Microsoft Word format contains the detailed results of the performance evaluation.
  datacite Wirth, Felix Nikolaus, Kussel, Tobias, Müller, Armin, Hamacher, Kay 等 2024 置信度 0.66 Computation Theory and MathematicsFOS: Computer and information sciencesComputer SoftwareData FormatInformation Systems
• Additional file 2 of EasySMPC: a simple but powerful no-code tool for practical secure multiparty computation
  Additional file 2. Microsoft Word format contains the detailed results of the performance evaluation.
  datacite Wirth, Felix Nikolaus, Kussel, Tobias, Müller, Armin, Hamacher, Kay 等 2024 置信度 0.66 Computation Theory and MathematicsFOS: Computer and information sciencesComputer SoftwareData FormatInformation Systems
• Additional file 1 of Sequre: a high-performance framework for secure multiparty computation enables biomedical data sharing
  Additional file 1. Sequre supplementary notes provides additional insight to Sequre, its usability and optimizations, and the results [61–90].
  datacite Smajlović, Haris, Shajii, Ariya, Berger, Bonnie, Cho, Hyunghoon 等 2024 置信度 0.66 Information SystemsFOS: Computer and information sciences
• Additional file 1 of Sequre: a high-performance framework for secure multiparty computation enables biomedical data sharing
  Additional file 1. Sequre supplementary notes provides additional insight to Sequre, its usability and optimizations, and the results [61–90].
  datacite Smajlović, Haris, Shajii, Ariya, Berger, Bonnie, Cho, Hyunghoon 等 2024 置信度 0.66 Information SystemsFOS: Computer and information sciences
• Additional file 2 of Sequre: a high-performance framework for secure multiparty computation enables biomedical data sharing
  Additional file 2. Review history.
  datacite Smajlović, Haris, Shajii, Ariya, Berger, Bonnie, Cho, Hyunghoon 等 2024 置信度 0.66 Information SystemsFOS: Computer and information sciences
• Additional file 2 of Sequre: a high-performance framework for secure multiparty computation enables biomedical data sharing
  Additional file 2. Review history.
  datacite Smajlović, Haris, Shajii, Ariya, Berger, Bonnie, Cho, Hyunghoon 等 2024 置信度 0.66 Information SystemsFOS: Computer and information sciences
• Multiparty Homomorphic Encryption: from Theory to Practice
  Multiparty homomorphic encryption (MHE) enables a group of parties to encrypt data in a way that (i) enables the evaluation of functions directly over its ciphertexts and (ii) enforces a joint cryptographic access-control over the underlying data. By extending…
  datacite Mouchet, Christian Vincent 2023 置信度 0.66 Multiparty homomorphic encryptionsecure multiparty computationthreshold access-structuresimplementations
• Secure Multiparty Computation with Dynamic Participants and Guaranteed Output
  Secure Multiparty Computation (MPC) is an expression encompassing cryptographic techniques that allow mutually distrustful parties to jointly perform computations over private data, ensuring correctness and preserving privacy despite malicious behavior. Guaran…
  datacite Deligios, Giovanni 2025 置信度 0.66
• vantage6
  Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains,…
  datacite Martin, Frank, Beusekom, Bart, Leurs, Richard, Sieswerda, Melle 等 2026 置信度 0.66 federated learningmachine learningdata analysissecure multiparty computationprivacy enhancing technology
• vantage6
  Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains,…
  datacite Martin, Frank, Beusekom, Bart, Leurs, Richard, Sieswerda, Melle 等 2026 置信度 0.66 federated learningmachine learningdata analysissecure multiparty computationprivacy enhancing technology
• Privacy-Preserving Machine Learning Technique Using One-Way Hashing for Enhancing Data Security in the Nigerian Healthcare Ecosystem
  Safeguarding healthcare data in Nigeria remains a pressing challenge, complicated by fragmented infrastructure, limited resources, and evolving regulatory frameworks. This paper presents a conceptual analysis of one-way hashing as a lightweight cryptographic t…
  datacite Damang, F.S., Aimufua, and Gilbert I.O. 2026 置信度 0.66 Healthcare data protection; Federated learning; One-way hashing; Pseudonymizations; Privacy-preserving machine learning; Conceptual analysis; Nigeria Data Protection Regulation (NDPR); Nigeria Data Protection Act (NDPA); Cryptographic complexity; Resource-constrained environments
• Privacy-Preserving Machine Learning Technique Using One-Way Hashing for Enhancing Data Security in the Nigerian Healthcare Ecosystem
  Safeguarding healthcare data in Nigeria remains a pressing challenge, complicated by fragmented infrastructure, limited resources, and evolving regulatory frameworks. This paper presents a conceptual analysis of one-way hashing as a lightweight cryptographic t…
  datacite Damang, F.S., Aimufua, and Gilbert I.O. 2026 置信度 0.66 Healthcare data protection; Federated learning; One-way hashing; Pseudonymizations; Privacy-preserving machine learning; Conceptual analysis; Nigeria Data Protection Regulation (NDPR); Nigeria Data Protection Act (NDPA); Cryptographic complexity; Resource-constrained environments
• Privacy-Preserving Machine Learning Technique Using One-Way Hashing for Enhancing Data Security in the Nigerian Healthcare Ecosystem
  Safeguarding healthcare data in Nigeria remains a pressing challenge, complicated by fragmented infrastructure, limited resources, and evolving regulatory frameworks. This paper presents a conceptual analysis of one-way hashing as a lightweight cryptographic t…
  datacite Damang, F.S., Aimufua, and Gilbert I.O. 2026 置信度 0.66 Healthcare data protection; Federated learning; One-way hashing; Pseudonymizations; Privacy-preserving machine learning; Conceptual analysis; Nigeria Data Protection Regulation (NDPR); Nigeria Data Protection Act (NDPA); Cryptographic complexity; Resource-constrained environments
• Privacy-Preserving Machine Learning Technique Using One-Way Hashing for Enhancing Data Security in the Nigerian Healthcare Ecosystem
  Safeguarding healthcare data in Nigeria remains a pressing challenge, complicated by fragmented infrastructure, limited resources, and evolving regulatory frameworks. This paper presents a conceptual analysis of one-way hashing as a lightweight cryptographic t…
  datacite Damang, F.S., Aimufua, and Gilbert I.O. 2026 置信度 0.66 Healthcare data protection; Federated learning; One-way hashing; Pseudonymizations; Privacy-preserving machine learning; Conceptual analysis; Nigeria Data Protection Regulation (NDPR); Nigeria Data Protection Act (NDPA); Cryptographic complexity; Resource-constrained environments
• End to End Collaborative Synthetic Data Generation
  The success of AI is based on the availability of data to train models. While in some cases a single data custodian may have sufficient data to enable AI, often multiple custodians need to collaborate to reach a cumulative size required for meaningful AI resea…
  datacite Pentyala, Sikha, Sitaraman, Geetha, Claar, Trae, De Cock, Martine 2024 置信度 0.66 Cryptography and Security (cs.CR)Machine Learning (cs.LG)FOS: Computer and information sciences
• FuSeFL: Fully Secure and Scalable Federated Learning
  Federated Learning (FL) enables collaborative model training without centralizing client data, making it attractive for privacy-sensitive domains. While existing approaches employ cryptographic techniques such as homomorphic encryption, differential privacy, o…
  datacite Ghinani, Sahar Ghoflsaz, Sadredini, Elaheh 2025 置信度 0.66 Cryptography and Security (cs.CR)Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• vantage6
  Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains,…
  datacite Martin, Frank, Beusekom, Bart, Leurs, Richard, Sieswerda, Melle 等 2026 置信度 0.66 federated learningmachine learningdata analysissecure multiparty computationprivacy enhancing technology
• vantage6
  Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains,…
  datacite Martin, Frank, Beusekom, Bart, Leurs, Richard, Sieswerda, Melle 等 2026 置信度 0.66 federated learningmachine learningdata analysissecure multiparty computationprivacy enhancing technology
• vantage6
  Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains,…
  datacite Martin, Frank, Beusekom, Bart, Leurs, Richard, Sieswerda, Melle 等 2026 置信度 0.66 federated learningmachine learningdata analysissecure multiparty computationprivacy enhancing technology
• Re-visiting Authorized Private Set Intersection: A New Privacy-Preserving Variant and Two Protocols
  We revisit the problem of Authorized Private Set Intersection (APSI), which allows mutually untrusting parties to authorize their items using a trusted third-party judge before privately computing the intersection. We also initiate the study of Partial-APSI, a…
  datacite Falzon, Francesca, Markatou, Evangelia Anna 2025 置信度 0.66 Private set intersection2PCSecure multiparty computation
• TEE-Based Distributed Ledgers and Their Resilience
  Resilience is the ability of a (distributed) system to withstand any stressful situation without imposing massive restrictions and, above all, without long-term consequences. Permissioned distributed ledgers based on state machine replication (SMR) offer a pro…
  datacite Leinweber, Marc 2026 置信度 0.66 Distributed Ledger TechnologyTrusted Execution EnvironmentsState Machine ReplicationDistributed Systems SecuritySecurity Evaluation
• An Automated Approach to Generating Card-Based Cryptographic Protocols
  Card-based cryptographic protocols provide a simple and illustrative way of performing multi-party computation without computers, but instead use just a set of playing cards. A lot of research has been done on finding minimal protocols, with respect to the num…
  datacite Hoff, Anne Elisabeth 2023 置信度 0.66 secure multiparty computationcard-based cryptographyformal verificationbounded model checking
• Learning-Augmented Perfectly Secure Collaborative Matrix Multiplication
  This paper presents a perfectly secure matrix multiplication (PSMM) protocol for multiparty computation (MPC) of $\mathrm{A}^{\top}\mathrm{B}$ over finite fields. The proposed scheme guarantees correctness and information-theoretic privacy against threshold-bo…
  datacite He, Zixuan, Salehi, Mohammad Reza Deylam, Malak, Derya, Stavrou, Photios A. 2026 置信度 0.66 Information Theory (cs.IT)Multiagent Systems (cs.MA)Systems and Control (eess.SY)FOS: Computer and information sciencesFOS: Computer and information sciences
• Federated Learning for eResearch
  As data privacy regulations tighten and datasets grow increasingly siloed across institutions, Federated Learning (FL) offers a transformative approach to collaborative machine learning in the eResearch domain. This talk introduces the core principles of Feder…
  datacite Marendy, Peter 2025 置信度 0.66 Research Data
• Federated Learning for eResearch
  As data privacy regulations tighten and datasets grow increasingly siloed across institutions, Federated Learning (FL) offers a transformative approach to collaborative machine learning in the eResearch domain. This talk introduces the core principles of Feder…
  datacite Marendy, Peter 2025 置信度 0.66 Research Data
• 3D-based contact-less fingerprint acquisition
  Fingerprint recognition is a widely used biometric modality because of its uniqueness and persistence of friction ridge patterns, which provide a reliable means of verifying identity. It is a key technology in applications ranging from law enforcement and bord…
  datacite Ruzicka, Laurenz 2025 置信度 0.66 Contactless fingerprint recognitiondeep learningbiometricssynthetic fingerprint phantoms