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• Parallel AI-driven framework for post-quantum secure medical image communication using swin-transformer restoration.
  Reliable and secure transmission of medical images is essential for telemedicine, remote diagnosis, and distributed healthcare systems. However, medical image communication over heterogeneous networks often suffers from packet loss, channel noise, and privacy …
  pubmed Alsuwat E 2026 May 23 置信度 0.82
• FedAK: a semi-supervised one-shot framework for heterogeneous federated learning via feature-level attention-based knowledge distillation.
  Federated learning (FL) enables multiple devices to collaboratively train a shared model while keeping their data localized, thereby preserving privacy. Despite its promise, FL continues to face key challenges such as model heterogeneity, high communication ov…
  pubmed Salman H, Pradat-Peyre JF, Guehis S, Charara N 等 2026 May 23 置信度 0.82
• Artificial intelligence-enhanced ultrasound multimodal imaging and tissue characterization for predicting immunotherapy efficacy in rectal cancer.
  Rectal cancer remains a global health burden, with immunotherapy emerging as a transformative treatment modality demonstrating substantial pathological complete response rates across molecular subtypes. However, current response assessment methods based on ana…
  pubmed Wang Q, Chen Y 2026 Oct 置信度 0.82
• From documentation to discovery: clinicians' perspectives on the generation, usability and standardization of real-world data.
  Real-world data (RWD) are increasingly recognized as essential for understanding patient populations underrepresented in clinical trials and for supporting data-driven learning in healthcare. For smaller subgroups, the value of RWD depends on standardization a…
  pubmed Ross E, Bouissou O, Helland Å, Faxvaag A 2026 Sep 1 置信度 0.82
• A Privacy Index Calculator for Federated Medical Studies.
  Sharing clinical datasets requires rigorous privacy guarantees to prevent subject re-identification. This paper presents a privacy analysis tool that operates entirely client-side, ensuring data protection by eliminating the need for server-side processing. Th…
  pubmed Gameiro J, Barros V, Almeida JR, Oliveira JL 2026 May 21 置信度 0.82
• A Maturity Model for the Enforcement of PETs in Federated Settings.
  We explore how privacy can be made both usable and enforceable within FLAME, a federated learning platform developed in the German PrivateAIM project. We propose a Privacy-Enhancing Technology (PET) Integration Maturity Model with three levels: Analysis-Based,…
  pubmed Abu Attieh H, Halilovic M, Herr MAB, Hieber D 等 2026 May 21 置信度 0.82
• Federated Deception Intelligence and the ASKOS Solution for Cyber-Resilient Healthcare Infrastructures.
  Healthcare infrastructures face escalating cybersecurity risks caused by legacy systems, connected IoMT devices, and complex data-sharing environments. Conventional intrusion detection and SIEM systems provide limited protection against zero-day and lateral-mo…
  pubmed Giannakopoulou O, Tarousi M, Androutsou T, Pitoglou S 等 2026 May 21 置信度 0.82
• Annotation-Driven Middleware for Laboratory Data Exchange: Building Federated and Equitable Health Information Systems.
  Reliable access to laboratory and diagnostic data is essential for safe and equitable healthcare, yet laboratory data exchange remains fragmented across institution-specific infrastructures. Laboratory Information Management Systems (LIMS) and electronic healt…
  pubmed Bamunuge S, Haddad T, Khaddaj S, de Lusignan S 等 2026 May 21 置信度 0.82
• Setting up a DataSHIELD Hub for the German Medical Informatics Initiative: Challenges and Lessons Learned.
  The secondary use of clinical routine data offers major opportunities for biomedical research but also poses challenges regarding data protection, interoperability, and coordination. Within the German Medical Informatics Initiative (MII), Data Integration Cent…
  pubmed Abu Attieh H, Jolly JK, Pallaoro P, de Arruda Botelho Herr M 等 2026 May 21 置信度 0.82
• IMPaCT-Data: A Federated Precision Medicine Infrastructure Associated with Science and Technology in Spain.
  In alignment with the European Health Data Space (EHDS), Spain's IMPaCT - Precision Medicine Infrastructure associated with Science and Technology - aims to establish a trusted research environment (TRE) for secure and FAIR data sharing and analysis. It is str…
  pubmed Rodríguez-Mejías S, Sánchez-Cabo F, Al-Shahrour F, Rosas C 等 2026 May 21 置信度 0.82
• Design of a Privacy-Preserving ETL Dataflow for Federated ICU Data Reuse in INDICATE.
  The INDICATE project, funded by the European Union under the Digital Europe Programme, develops a federated framework for the secure reuse of intensive care unit (ICU) data within the EHDS. This work presents the design of a privacy-preserving ETL dataflow tha…
  pubmed Parra Rodriguez-Armijo M, Alvarez-Romero C, van den Brand J, Delange B 等 2026 May 21 置信度 0.82
• Towards an Integration Engine to Achieve Federated Semantic Interoperability.
  Efficient and semantically correct data exchange between healthcare systems remains a major challenge due to heterogeneous data models, legacy technologies, and inconsistent adoption of standards. This research investigates and develops an open-source integrat…
  pubmed Randmaa R, Bossenko I, Piho G, Ross P 2026 May 21 置信度 0.82
• Practical Implications for Using Laboratory Data: Research over Federated Networks.
  Semantic standardization is essential, but not sufficient, to enable research over real-world data networks. Even when harmonized, differences in data availability, granularity, and clinical practice can introduce subtle biases that affect downstream analyses …
  pubmed Rubio Ruiz D, Muñoz Monjas A, Bermejo Bernardo P, Perez-Rey D 等 2026 May 21 置信度 0.82
• A Federated Benchmark for Clinical Natural Language Processing (FedDRAGON).
  We introduce the FedDRAGON challenge, a federated learning benchmark for clinical natural language processing. The challenge includes 12 information extraction tasks where data is extracted from clinical reports from 4 Dutch care centers. Baseline results show…
  pubmed Abrahamsen BS, Bosma JS, Huisman H, Elschot M 2026 May 21 置信度 0.82
• Federated Multi-Agent Architecture for Harmonizing Public Health Datasets into OMOP and FHIR Standards.
  This study presents a federated system developed within the SHIELD project, part of Horizon Europe's effort to reduce non-communicable diseases. It integrates retrospective and prospective clinical data using LLM-based multi-agent systems for automated ETL and…
  pubmed Lozano F, Sánchez Esquivel J, Paraíso-Medina S, Alonso-Calvo R 等 2026 May 21 置信度 0.82
• Federated Propensity Score Matching for Bias Correction in ICU.
  Multi-site medical studies require methods that address confounding bias while respecting data privacy regulations. We propose a federated learning method integrating propensity score matching (PSM) to achieve both objectives simultaneously. Using data from fi…
  pubmed Sheikhalishahi S, Schwinn J, Morhart M, Hinske LC 等 2026 May 21 置信度 0.82
• Client Participation per Round in Federated Learning for Multiple Sclerosis with Real-World Data.
  We quantify how the per round client participation rate k affects performance and runtime in a federated learning study predicting two year confirmed disability progression in multiple sclerosis using routine clinical data. Using the original study's data, pre…
  pubmed Pirmani A, MSBase Study Group, Moreau Y, Peeters LM 2026 May 21 置信度 0.82
• Building an Ontology-Based Cohort of Liver Cancer Imaging Data for AI Development on the European Federated Platform EUCAIM.
  Hepatocellular carcinoma (HCC) is steadily increasing in incidence worldwide and requires data-driven approaches to improve diagnosis, prognosis, and therapeutic decisions. We describe the harmonization of IMALIVE -a real-world HCC cohort- with the common data…
  pubmed Guedjali A, Mondet K, Beaufrere A, Gregory J 等 2026 May 21 置信度 0.82
• Effects of Non-IID Distributions in Lung Cancer Data on Survival Prediction with Federated Ensemble Learning.
  A common challenge in Federated Learning (FL) is that distribution shifts between clients, or Non-IIDness, decrease global model performance. Non-IIDness means that data is not independently and identically distributed between participating sites. Stronger dis…
  pubmed Weber L, Hauschild AC, Altenbuchinger M, Sax U 等 2026 May 21 置信度 0.82
• Preoperative Risk Assessment of Adrenal Metastases in a Multicenter Study: Development of a Robust Federated Learning Model.
  In clinical practice, the preoperative risk assessment of adrenal metastases versus benign adrenal lesions carries a substantial risk of misdiagnosis. The artificial intelligence technology holds promise for reducing misdiagnosis rates. However, due to the pro…
  pubmed Feng B, Yu Z, Chen Y, Xu J 等 2026 Sep 置信度 0.82
• Artificial intelligence in multi-omics analysis of small-molecule drug discovery.
  Artificial intelligence (AI) is transforming multi-omics analysis in small-molecule drug discovery by advancing vast datasets from genomics, transcriptomics, proteomics, and metabolomics to cover novel therapeutic targets and optimize lead compounds. Machine l…
  pubmed Soni S, Rathee S, Sreeharsha N, Vasdev N 等 2026 置信度 0.82
• Hybrid fractional groupers and moray eels driven deep learning for pneumonia detection using multi-modal data in federated learning.
  Pneumonia is a severe lung infection triggered by various viral pathogens. Detecting and diagnosing pneumonia using clinical images is challenging because its visual features often resemble those of other pulmonary conditions. As a result, existing approaches …
  pubmed L L S M, Bharti P 2026 Jul 15 置信度 0.82
• Recovering Reward Functions From Distributed Expert Demonstrations via Bi-Level Maximum-Likelihood Optimization.
  Inverse reinforcement learning (IRL) seeks to infer the latent reward function and the associated optimal policy from expert demonstrations. However, most current IRL methods assume centralized access to all trajectory data, which is impractical in real-world …
  pubmed Jiang G, Hong S, Imani M, Bastian ND 等 2026 May 19 置信度 0.82
• Integrated Platforms to Further Advance Space Biology Research.
  Space biology research increasingly requires integrated platforms capable of managing complex datasets. This chapter introduces the NASA Open Science Data Repository (OSDR), designed to unify multiomics, physiological, environmental telemetry, and radiation ex…
  pubmed Sanders LM, Costes SV 2026 置信度 0.82
• HybridTrust: on-device federated learning with crypto-agile security for legacy and quantum-safe medical devices.
  The process of migration of IoMT systems in healthcare into post-quantum cryptographic systems is expected to be a gradual one. Here, existing ECC-based devices alongside newly developed quantum-resistant devices will be operating within the same healthcare ec…
  pubmed Khan UH, Khan R, Chelloug SA, Alturise F 等 2026 置信度 0.82
• Barrier check study: why predictive machine learning struggles to reach the operating room.
  Machine learning (ML) has the potential to enhance surgical decision-making through real-time risk prediction and personalised care yet clinical implementation remains limited. This study aimed to identify and categorise the key barriers to implementing ML too…
  pubmed Ben Hmido S, Garita C, Bloemers F, Rozie S 等 2026 May 18 置信度 0.82
• The Data Science of Health in Space.
  Spaceflight exposes humans to unique stressors that remodel biology. Space health is emerging as a data science discipline grounded in multiomic atlases, harmonized biobanks, and open, findable, accessible, interoperable, and reusable (FAIR)-aligned infrastruc…
  pubmed Willett JDS, Kim J, Sakharkar A, Park J 等 2026 Aug 置信度 0.82
• Toward Privacy Preservation in Federated Learning: A Framework Integrating Client-Side Shuffling and Model Compression.
  Federated learning (FL) enables multiple clients to train models on local data and collaboratively optimize a global model without sharing raw data. However, client heterogeneity, such as differences in data distributions and system capabilities, poses challen…
  pubmed Li J, Xiao R, Yu L, Zhang K 等 2026 May 18 置信度 0.82
• Deep learning-based cognitive impairment brain imaging analysis: New methods, new technologies, and new paradigms.
  Cognitive impairment arising from ischemic stroke, Alzheimer's disease, and Parkinson's disease presents distinct structural and network-level alterations. Brain magnetic resonance imaging offers a non-invasive and high-resolution approach to assess these chan…
  pubmed Xu Q, Lu J, Zhang Z, Xu D 等 2026 Sep 1 置信度 0.82
• Training Together, Diagnosing Better: Federated Learning for Collagen VI-Related Dystrophies.
  The application of Machine Learning (ML) to the diagnosis of rare diseases, such as collagen VI-related dystrophies (COL6-RD), is fundamentally limited by the scarcity and fragmentation of available data. Attempts to expand sampling across hospitals, instituti…
  pubmed Brull A, Aguti S, Bolduc V, Hu Y 等 2025 Dec 18 置信度 0.82
• Current trends and future directions of artificial intelligence in lung cancer diagnosis.
  Lung cancer is the most lethal malignancy worldwide, largely due to its late detection after its progression to advanced stages. Over the last decade, artificial intelligence (AI) applications have shown significant potential in transforming lung cancer diagno…
  pubmed Hu W, Wang G, Ren L, Hu J 等 2026 Apr 30 置信度 0.82
• Open neuroinformatics infrastructure ecosystem for federated multisite studies.
  Despite growing understanding of the benefits of having Findable, Accessible, Interoperable, and Reusable (FAIR) data, many datasets still cannot be shared. Federated analysis methods can enable multisite studies that do not require the sharing of participant-…
  pubmed Wang M, Bhagwat N, Cremonesi F, Dugré M 等 2026 置信度 0.82
• NIPTE 2030: The Nation's Digital Trust Anchor for Pharma 5.0.
  As the National Institute for Pharmaceutical Technology and Education (NIPTE) marks its 20th anniversary, the pharmaceutical ecosystem faces a widening gap between regulatory "risk-to-quality" compliance and real-world "risk-to-patient" outcomes. This Perspect…
  pubmed Hussain AS, Morris K, Gurvich VJ 2026 May 15 置信度 0.82
• Quantum-enhanced federated blockchain for privacy-preserving cardiovascular intelligence.
  Cardio-Vascular Diseases (CvDs) persist as a significant mortality reason worldwide, which requires advanced risk categorization technologies that can offer custom medicine while protecting patient information. Present healthcare approaches must overcome fragm…
  pubmed Sivakami R, Kumar VV, Krishnamoorthy N, Yimer TE 2026 May 16 置信度 0.82
• Blockchain and Federated Learning for Remote Health Monitoring
  Digital technologies are revolutionizing healthcare, particularly in Remote Health Monitoring (RHM). RHM uses digital tools to monitor patients’ health data outside traditional clinical settings, allowing for continuous real-time management. This approach has …
  crossref Venkatesh Upadrista 2025-04-17T14:43:28Z 置信度 0.70
• Figure 17: Error analysis of federated-learning approach (Algorithm 2).
  crossref 2026-05-01T08:00:31Z 置信度 0.70
• Federated learning
  crossref Jay Gajera, Henry Knipe, James Condon 2021-10-25T02:51:36Z 置信度 0.70
• Towards private and efficient cross-device federated learning
  crossref Zhifeng Jiang 2024-10-17T01:55:46Z 置信度 0.70
• Incentive Mechanism Design for Federated Learning
  crossref Qiang Yang, Yang Liu, Yong Cheng, Yan Kang 等 2022-06-09T04:46:22Z 置信度 0.70
• Federated Learning for Smart Agricultural Supply Chains
  crossref Mamta 2026-06-23T12:51:06Z 置信度 0.70
• Table 4: Scalability analysis of blockchain operations in federated learning.
  crossref 2026-02-04T08:57:09Z 置信度 0.70
• Robust federated learning with attack-adaptive aggregation
  crossref Ching Pui Wan 2022-03-29T00:12:52Z 置信度 0.70
• Byzantine-Robust Federated Learning under Heterogeneity: Personalization and Transfer as a Way Forward?
  crossref Rafael Pinot 2026-05-22T12:25:02Z 置信度 0.70
• Deep Federated Learning Based on Knowledge Distillation and Differential Privacy
  crossref Hui Lin, Feng Yu, Xiaoding Wang 2023-12-02T00:27:27Z 置信度 0.70
• FL-EHDS: A Privacy-Preserving Multimodal Federated Learning Framework for the European Health Data Space
  crossref Fabio Liberti 2026-07-29T19:11:37Z 置信度 0.70
• Introduction to Federated Learning
  crossref Mohit Pandey, Shubhangi Pandey, Ajit Kumar 2022-02-02T12:03:00Z 置信度 0.70
• Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning
  Abstract Federated learning is a new learning paradigm that decouples data collection and model training via multi-party computation and model aggregation.As a flexible learning setting, federated learning has the potential to integrate with other learning fra…
  preprints Shaoxiong Ji, Yue Tan, Teemu Saravirta, Zhiqin Yang 等 2023 置信度 0.74
• Collaborative Fairness in Federated Learning
  crossref Lingjuan Lyu, Xinyi Xu, Qian Wang, Han Yu 2020-11-25T20:03:24Z 置信度 0.70
• Copyright
  crossref 2025-09-12T12:25:54Z 置信度 0.70
• Federated Regularization Learning: an Accurate and Safe Method for Federated Learning
  crossref Tianqi Su, Meiqi Wang, Zhongfeng Wang 2021-06-23T20:01:10Z 置信度 0.70
• Anonymous Communication and Shuffle Model in Federated Learning
  crossref Shui Yu, Lei Cui 2023-03-26T21:30:41Z 置信度 0.70
• IEEE Guide for Framework for Trustworthy Federated Machine Learning
  crossref 2024-12-18T14:28:06Z 置信度 0.70
• Effective and secure federated online learning to rank
  crossref Shuyi Wang 2024-11-22T01:25:15Z 置信度 0.70
• Figure 8: Comparison of using reputation management in federated learning.
  crossref 2026-02-04T08:57:09Z 置信度 0.70
• Privacy and Security Enhanced Federated Learning Framework Design
  crossref Yasaman Pakdel 2024-07-02T19:22:20Z 置信度 0.70
• Federated Learning for Open Banking
  crossref Guodong Long, Yue Tan, Jing Jiang, Chengqi Zhang 2020-11-25T15:03:24Z 置信度 0.70
• A Contract Theory Based Incentive Mechanism for Federated Learning
  crossref Yuan Liu, Mengmeng Tian, Yuxin Chen, Zehui Xiong 等 2022-09-30T18:05:01Z 置信度 0.70
• Quantum Federated Learning for Wireless Communications
  crossref R. M. Pujahari, Akshit Tanwar 2022-02-02T12:03:00Z 置信度 0.70
• Federated Learning for Vision, Language, and Recommendation
  crossref Qiang Yang, Yang Liu, Yong Cheng, Yan Kang 等 2022-06-09T04:17:40Z 置信度 0.70
• Heterogeneity-Aware Theory and Algorithms for Federated Reinforcement Learning
  crossref Zhijie Xie 2026-03-12T23:02:19Z 置信度 0.70
• Budget-Bounded Incentives for Federated Learning
  crossref Adam Richardson, Aris Filos-Ratsikas, Boi Faltings 2020-11-25T20:03:24Z 置信度 0.70
• Federated Learning Versus Classical Machine Learning: A Convergence Comparison
  In the past few decades, machine learning has revolutionized data processing for large scale applications. Simultaneously , increasing privacy threats in trending applications led to the redesign of classical data training models. In particular, classical mach…
  crossref Muhammad Asad, Ahmed Moustafa, Takayuki Ito 2021-05-11T11:54:23Z 置信度 0.70
• Federated learning: an introduction [report]
  crossref Anastasia Shteyn, Konrad Kollnig, Calum Inverarity 2024-05-22T18:58:46Z 置信度 0.70
• Secure and Private Federated Learning through Encrypted Parameter Aggregation
  crossref K Vijayalakshmi, PM Sitharselvam, I Thamarai, J Ashok 等 2023-11-16T11:04:28Z 置信度 0.70
• Federated Learning at the Edge: Enabling AI in 5G/6G Networks and Massive IoT
  Federated learning (FL) at the edge is emerging as a cornerstone of next-generation AI, particularly in the era of 5G and 6G connectivity. This chapter explores how FL enables distributed devices and edge servers to collaboratively train AI models across massi…
  crossref Iacovos Ioannou, Christophoros Christophorou, Vasos Vassiliou 2026-03-25T14:08:56Z 置信度 0.70
• Trusted Federated Learning Solutions for Internet of Medical Things
  crossref Sagar Lakhanotra, Jaimik Chauhan, Vivek Kumar Prasad, Pronaya Bhattacharya 2023-05-03T20:41:25Z 置信度 0.70
• Adaptive Evolutionary Clustering for Federated Learning under Nonstationary Client Distributions
  crossref Yiyue Chen, Usman Akram, Chianing Wang, Haris Vikalo 2026-07-29T19:14:43Z 置信度 0.70
• Bias and Fairness in Federated Learning for Healthcare
  crossref Pratikkumar Dalsukhbhai Korat 2026-02-17T19:02:56Z 置信度 0.70
• Federated Learning: A Comprehensive Review of Methods, Challenges, Applications and Trends
  Distributed data collaboration allows machine learning models to be trained on multiple independent sources without compromising confidential information. This approach, known as federated learning (FL), represents a revolutionary shift in how artificial intel…
  crossref Babak Basharirad, Sunil Choenni, Mortaza S. Bargh and Ahmad Omar 2026-05-14T12:45:32Z 置信度 0.70
• An Empirical Analysis of Adversarial Attacks in Federated Learning
  crossref Rohit Mapakshi 2025-04-01T13:42:04Z 置信度 0.70
• Privacy and profit: the dual benefits of federated learning in metaverse healthcare systems
  crossref Manas Kumar Yogi, Likhita Nam, Pavani Adina, Lolla Venkata Manaswini Rajeswari 2025-09-12T12:25:54Z 置信度 0.70
• Cyber Threat Detection in IoT-Enabled Edge Computing Using Optimized Federated Deep Learning
  crossref V.R. Ramesh Babu, G. Jayamurugan, R. Vijayarangan, V. Thirumurgan 2026-05-08T21:41:08Z 置信度 0.70
• Privacy-preserving detection of poisoning attacks in federated learning
  crossref Trent Muhr 2025-05-02T19:34:42Z 置信度 0.70
• Secure and Resilient Federated Learning for Malware Classification
  crossref Chandrakanth Reddy Challa 2026-07-31T21:09:09Z 置信度 0.70
• Machine learning and neural methods for federated patent search
  Η παρούσα διδακτορική διατριβή διερευνά τον εξειδικευμένο και ιδιαίτερα απαιτητικό τομέα της ανάκτησης πληροφοριών από διπλώματα ευρεσιτεχνίας (πατέντες), με στόχο τη γεφύρωση του χάσματος μεταξύ των παραδοσιακών μεθοδολογιών αναζήτησης και των σύγχρονων δυνατ…
  crossref Βασίλειος Σταμάτης 2026-05-07T08:10:12Z 置信度 0.70
• An Intelligent Decentralized Framework for Next-Generation IoT Security Using Blockchain and Federated Learning
  crossref MUTHUPANDI G, MALLIGA M, THIRUMALAI MURUGAN, Vignesh J 2026-05-03T20:52:58Z 置信度 0.70
• Towards Byzantine-Resilient Federated Learning via Group-Wise Robust Aggregation
  crossref Lei Yu, Lingfei Wu 2020-11-25T20:03:24Z 置信度 0.70
• Some Observations on the Behaviour of Federated Learning
  crossref Vishal Kaushal, Sangeeta Sharma 2022-02-02T12:03:00Z 置信度 0.70
• Federated Learning for IoT Devices
  crossref Deena Nath Gupta, Rajendra Kumar, Ashwani Kumar 2022-02-02T12:03:00Z 置信度 0.70
• Large-Scale Kernel Method for Vertical Federated Learning
  crossref Zhiyuan Dang, Bin Gu, Heng Huang 2020-11-25T20:03:24Z 置信度 0.70
• Explainable Federated Learning for Taxi Travel Time Prediction
  crossref Jelena Fiosina 2022-03-03T09:15:40Z 置信度 0.70
• Federated Learning with Cooperating Devices: A Consensus Approach
  crossref Radhika Vadhi, Abhishek Sharma 2022-02-02T12:03:00Z 置信度 0.70
• Enabling privacy-preserving concept stock recommendation with federated learning
  crossref Zhuoyi Peng 2022-03-28T04:49:25Z 置信度 0.70
• Secure embedding aggregation for cross-silo federated representation learning
  crossref Jiaxiang Tang 2023-09-21T22:45:02Z 置信度 0.70
• FLAM: Evaluating Model Performance with Aggregatable Measures in Federated Learning
  crossref Fabian Stricker, Jose A. Peregrina, David Bermbach, Christian Zirpins 2026-07-29T19:10:57Z 置信度 0.70
• Explainable Federated Learning for Taxi Travel Time Prediction
  crossref Jelena Fiosina 2021-05-07T07:35:10Z 置信度 0.70
• Federated Learning Systems for Healthcare: Perspective and Recent Progress
  crossref Yogesh Kumar, Ruchi Singla 2021-06-10T23:42:05Z 置信度 0.70
• Unlocking Value of Data with Vertical Federated Learning
  crossref Khan Afsana 2026-07-06T15:07:37Z 置信度 0.70
• Figure 1: Workflow of ERP software design personalization using federated learning.
  crossref 2026-04-02T08:53:41Z 置信度 0.70
• Studies on biomolecular structure learning and federated privacy protection
  crossref Hanlin Gu 2023-09-21T22:45:02Z 置信度 0.70
• Future of Medical Research with a Data-driven Federated Learning Approach
  crossref G. Arun Sampaul Thomas, S. Muthukaruppasamy, S. Sathish Kumar, K. Saravanan 2023-11-16T11:04:28Z 置信度 0.70
• Improvement of Federated Learning Models for E-health
  Amélioration des modèles d’apprentissage fédéré pour la cybersanté L'apprentissage fédéré (FL) est devenu un paradigme prometteur pour l'apprentissage automatique collaboratif, permettant à plusieurs participants d'entraîner des modèles partagés tout en préser…
  crossref Hassan Salman 2026-06-18T08:15:11Z 置信度 0.70
• Vertical Federated Learning for Cerebrovascular Accident Outcome Prediction
  Accurate and early predictions of CVA outcomes are critical for informing patients and healthcare professionals and help with creating personalized rehabilitation plans that aid in guiding treatment decisions and improving patient outcomes. Artificial Intellig…
  crossref Corinne Geertruida Allaart 2025-05-14T22:01:05Z 置信度 0.70
• Analog Over‐the‐Air Federated Learning: Design and Analysis
  crossref Howard H. Yang, Zihan Chen, Tony Q. S. Quek 2023-12-02T00:27:27Z 置信度 0.70
• Improving participants’ competitiveness in an open federated learning environment
  crossref Xavier Tan 2025-09-05T06:52:11Z 置信度 0.70
• Attacks and defenses in federated learning: a client perspective
  crossref Mengyao Ma 2026-03-02T04:20:41Z 置信度 0.70
• User‐Centric Decentralized Federated Learning for Autoencoder‐Based CSI Feedback
  crossref Shi Jin, Jiajia Guo, Yan Lv, Yiming Cui 2023-12-02T00:27:27Z 置信度 0.70
• Optimized Federated Learning in Wireless Networks with Constrained Resources
  crossref 2022-06-16T00:05:40Z 置信度 0.70
• Stakeholder-oriented decision support in auction-based federated learning
  crossref Xiaoli Tang 2025-07-11T08:48:03Z 置信度 0.70
• Unsupervised Federated Learning
  crossref Choong Seon Hong, Latif U. Khan, Mingzhe Chen, Dawei Chen 等 2022-01-01T05:02:09Z 置信度 0.70