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• Social Federated Learning (SFL): Leveraging Shared Data to Boost Learning Performance
  Collaboration between edge devices has the potential to scale up machine learning (ML) by enabling access to unprecedented amounts of data. Federated learning (FL) is a collaborative algorithm in which clients learn from each other without sharing private data…
  crossref Mahran Jazi, Ilai Bistritz, Nicholas Bambos, Irad Ben-Gal 2026-03-05T15:43:30Z 置信度 0.70
• A Scalable Federated Deep Reinforcement Learning Architecture for Collaborative Learning
  Federated Learning enables collaborative model training without sharing raw data, while Deep Reinforcement Learning provides powerful mechanisms for sequential decision-making. However, their integration suffers from limited scalability, sensitivity to non-IID…
  crossref Tarek Haddad 2026-03-27T23:45:23Z 置信度 0.70
• Federated Temporal RAG for Privacy-Preserving Real-Time Threat Intelligence in Distributed Remote Patient Monitoring
  crossref Mohammad Zahangir Alam, Elahan Ayath 2026-07-29T19:11:29Z 置信度 0.70
• Overview of Adversarial AI and Data Poisoning in Federated Learning
  Federated Learning (FL) is an emerging decentralized machine learning paradigm that enables multiple clients to collaboratively train a global model without sharing raw data, thereby preserving data privacy. . In adversarial settings, malicious clients can inj…
  crossref Mohammed Firdos Alam Sheikh 2026-02-20T15:26:17Z 置信度 0.70
• AirComp-Coordinated Q-Learning-Based Wireless Federated Learning for Household Load Forecasting
  Federated learning enables privacy-preserving short-term household load forecasting by keeping fine-grained electricity consumption data on local devices. However, its deployment over wireless networks is constrained by communication overhead, time-varying cha…
  crossref Zhangyan Ju, zhenping chen, Yihong Zhou, You Lu 2026-04-10T15:44:53Z 置信度 0.70
• Federated Forecasting of Urban Traffic Congestion with Graph Neural Network and Attention-Based Recurrent Neural Network
  crossref Javier Rodrigues, Sukhjit Singh Sehra 2026-07-29T19:12:32Z 置信度 0.70
• Federated Imputation under Heterogeneous Feature Spaces
  crossref Imane Hocine, Chaimaa Medjadji, Sylvain Kubler, Grégoire Danoy 等 2026-07-29T19:11:44Z 置信度 0.70
• Deep Learning and Federated Learning in breast cancer screening
  crossref 2026-05-11T11:20:16Z 置信度 0.70
• Defense Strategies in Federated Learning Against Adversarial Attacks
  crossref Hadiseh Rezaei, Rahim Taheri, Ehsan Nowroozi 2026-01-21T14:48:30Z 置信度 0.70
• Blockchain and Federated Learning for Next-Generation IoT Security
  crossref 2026-05-03T20:52:58Z 置信度 0.70
• QGuardian: A Federated Learning Framework for Quantum-Resilient Anomaly Detection in Cybersecurity
  Current cybersecurity systems face two critical challenges: the impending threat of quantum computers breaking classical cryptographic algorithms, and the limitations of reactive, centralized security architectures. We propose QGuardian, a federated learning f…
  crossref Ahaan Thota, Saketh Tammisetti 2026-01-12T15:08:42Z 置信度 0.70
• Reducing Computational Overhead in Federated Learning: A Comprehensive Analysis
  crossref Indraneel Mukhopadhyay, Debarpita Santra, Bannishikha Banerjee 2026-02-06T05:46:35Z 置信度 0.70
• Federated Learning in Agriculture
  crossref Mude Nagarjuna Naik, R. Sriramkumar, Joshuva Arockia Dhanraj, M. Lakshmanan 等 2026-06-23T12:51:06Z 置信度 0.70
• Decentralized Threat Intelligence Fusion using Federated Learning in Smart Agriculture and Smart Grids
  crossref Phillip Ben 2026-01-22T17:13:04Z 置信度 0.70
• Privacy-Preserving Federated Learning for Diabetes Risk Prediction Across Demographically Heterogeneous Hospital Nodes
  Background and Objective: Centralised machine learning for diabetes risk prediction conflicts with patient privacy regulations and produces models with poor demographic equity. We develop and evaluate a privacy-preserving federated learning (FL) framework for …
  crossref Rajveer Pall 2026-08-23T22:22:08Z 置信度 0.70
• Introduction to metaverse healthcare
  crossref Neha Gupta, Kavita Arora, Sailesh Suryanarayan Iyer 2025-09-12T12:25:54Z 置信度 0.70
• Fed-GCE: Federated learning with global constraints enforcement
  crossref Koffka Khan, Wayne Goodridge 2026-06-01T15:25:02Z 置信度 0.70
• Byzantine Resilient Federated Multi-Task Representation Learning
  crossref Tuan Le, Shana Moothedath 2026-07-14T19:38:09Z 置信度 0.70
• Securing digital payments and transactions using federated learning
  crossref Vikram Singh, Yashasvi Makin, Srikanth Yerra, Ravi Prakash Chaturvedi 等 2026-03-25T15:39:21Z 置信度 0.70
• Federated Learning Systems for Mobile Data
  crossref Xiaopeng Jiang 2026-07-31T18:12:10Z 置信度 0.70
• Federated learning-enhanced digital twin for intelligent warehouse management and demand forecasting
  crossref Yuqi Fu 2026-08-14T14:55:36Z 置信度 0.70
• Future Trends in Federated Learning: Enabling Secure and Personalized Healthcare Solutions
  crossref Randhir Singh Baghel, Udit Mamodiya 2026-02-06T05:46:58Z 置信度 0.70
• Neuro-Symbolic Federated Learning Models for Diagnostic Intelligence in Healthcare 5.0
  crossref Indra Kishor, Udit Mamodiya 2026-02-06T05:46:56Z 置信度 0.70
• Heterogeneity-Aware Federated Learning: From FedAvg to Multi-Task Personalization
  crossref Lakshmi Rangayya Naidu Kandulapati 2026-07-10T19:36:45Z 置信度 0.70
• Proposing a Federated Learning Policy Framework for Smart, Secure, and Sustainable Agricultural Supply Chains
  crossref Joshuva Arockia Dhanraj, M. Lakshmanan, A. Vegi Fernando, Mitha Guru 等 2026-06-23T12:51:06Z 置信度 0.70
• Advancing Federated Learning in Healthcare 5.0—A Futuristic Pathway in Healthcare
  crossref Geetha Manoharan, Sanjeev Kumar 2026-02-06T05:46:44Z 置信度 0.70
• Federated Clinical Learning Cooperative (FCLC)
  Background: Developing robust clinical artificial intelligence (AI) models requires large, diverse datasets that individual institutions cannot provide due to privacy regulations (GDPR, HIPAA) and institutional risk aversion. Existing federated learning (FL) p…
  openalex Jaba Tkemaladze 2026-04-26 置信度 0.72 Computer scienceDifferential privacyFederated learningPreprocessorOracle
• Federated Representation Learning-Empowered Deep Reinforcement Learning for Cloud-Edge Collaborative Heterogeneous Job Shop Scheduling
  Dynamic job shop scheduling in cloud–edge collaborative manufacturing is challenged by heterogeneous factory configurations, dynamic disturbances, and privacy constraints. Conventional federated reinforcement learning methods are difficult to apply directly be…
  crossref Jianguo Duan, Fangrong Chen, Qinglei Zhang 2026-06-10T06:43:08Z 置信度 0.70
• Adaptive Energy Management in Healthcare IoT Devices Using Federated Intelligence
  crossref S. K. Susee, M. Senthil Kumar, B. Chidhambararajan 2026-05-08T21:41:08Z 置信度 0.70
• Communication- and Inference-Efficient Federated Learning for Polyp Segmentation
  Background and ObjectiveAutomatic polyp segmentation in colonoscopy images plays an important role in computer-aided diagnosis and early detection of colorectal cancer. Most deep learning approaches rely on centralized training, which requires sharing medical …
  crossref Madan Baduwal, Priyanka Paudel, Tilak Neupane 2026-03-19T17:48:57Z 置信度 0.70
• Constructing a Personalized Learning Path Recommendation Model Based on Federated Learning
  To address the conflict between model generalization and personalized recommendation in distributed educational environments, this paper constructs a personalized learning path recommendation model based on federated learning. Such privacy-preserving collabora…
  crossref J. Zhang 2026-08-14T10:12:43Z 置信度 0.70
• Architectural Models and Security Considerations for Federated Learning
  The concept of Federated Learning (FL) has developed of centralized model averaging to a multi-faceted ecosystem of architectures, such as cross-device, cross-silo, hierarchical, and decentralized systems. Although the models allow collaboration of intelligenc…
  crossref Rachana Yogesh Patil, Yogesh H. Patil, Ozen Ozer 2026-02-20T15:26:17Z 置信度 0.70
• Federated Learning and Data Privacy in Connected Healthcare Devices
  Federated learning has emerged as a transformative paradigm for secure and intelligent healthcare systems, enabling collaborative model training without centralized data aggregation. This book chapter explores the architectural foundations, privacy-preserving …
  crossref A Thanikasalam, S Bharathi, Amit Kumar Bhakta 2026-01-20T12:38:32Z 置信度 0.70
• Analyzing Adversarial Attacks on Federated Learning based Medical Image Classification
  crossref Soomaiya Hamid, Narmeen Zakaria Bawany 2026-05-21T19:40:47Z 置信度 0.70
• Ethical and Technical Foundations of Privacy‐Preserving Federated Learning
  crossref Muhammad Rifthy Kalideen 2026-06-26T23:10:18Z 置信度 0.70
• Addressing Non-IID Challenges in Federated Learning with Transfer Learning
  crossref Rakshavi Dessai, Pravati Swain 2026-03-30T20:03:33Z 置信度 0.70
• Federated Learning Computational Cost Analysis
  crossref Ponmalar Ramanathan, Rashmi Welekar 2026-08-10T19:12:50Z 置信度 0.70
• Federated Adversarial Transfer Learning
  Federated Learning (FL) allows multiple parties to train a model on their local data source without exchanging raw data. When combined with FL, it provides a strong ability involving knowledge transfer from similar tasks with small data. This chapter reviews t…
  crossref Sanjeev Kumar, Geeta Tiwari, Laxmikant Sagar, Muhammad Attique Khan 2026-02-20T15:26:17Z 置信度 0.70
• Poisoning Resilient Federated Learning Under Privacy Constraints
  Federated learning enables multiple clients to collaboratively train models without exchanging raw data; however, the system remains susceptible to adversarial attacks through maliciously crafted model updates. This challenge intensifies as privacy-preserving …
  crossref Kummagoori Bharath, Pooja Chopra 2026-02-20T15:26:17Z 置信度 0.70
• Federated Learning-Based Personalized Music Recommendation System with Edge Caching in Cloud-Integrated Environments
  crossref Hemanta Ghosh 2026-05-20T19:49:13Z 置信度 0.70
• FedRPT: a Dual-Layer Federated Learning Framework for Copyright Protection
  crossref Haorui Wang, Guorui Feng 2026-08-17T19:15:53Z 置信度 0.70
• Detecting Malicious Clients in Federated Learning
  crossref Saikat Sinha Ray, Karthikeyan Periyasami 2026-04-23T19:57:35Z 置信度 0.70
• Decentralized and Secure Aggregation in Federated Learning
  crossref Jiaming Fang 2026-08-25T19:18:40Z 置信度 0.70
• Swarm Learning: A Comprehensive Overview and Its Potential in Cybersecurity
  crossref Diogo Oliveira, Apekshas Kafle 2026-07-29T19:09:35Z 置信度 0.70
• FedConv: Enhancing Convolutional Neural Networks for Handling Data Heterogeneity in Federated Learning
  crossref Dhanush Gopal Battina 2026-03-31T19:49:17Z 置信度 0.70
• Towards Adaptive and Communication-Efficient Dynamic Federated Learning
  crossref Shunxin Guo, Jiaqi Lv, Qiufeng Wang, Xin Geng 2026-07-02T16:48:01Z 置信度 0.70
• Federated Learning and Healthcare 5.0: Paving the Road Ahead for Privacy-Preserving Smart Health Systems
  crossref Wasswa Shafik 2026-02-06T05:46:44Z 置信度 0.70
• Vertical Federated Learning in Medical AI: Concepts, Advances, and Challenges
  The growing need for data-driven medical intelligence is constrained by strict privacy regulations and fragmented healthcare data distributed across multiple institutions. Vertical Federated Learning (VFL) offers a secure solution by enabling collaborative mod…
  crossref Muhammad Yaqub, Degang Xu, Lan He 2026-07-29T13:36:57Z 置信度 0.70
• Federated Learning for Healthcare
  crossref R. Anandan, Souvik Pal, D. Balaganesh, Farshad Badie 2026-05-08T21:41:08Z 置信度 0.70
• Federated Explainable Deep Learning Framework for Zero-Day Attack Detection in Encrypted Traffic Environments
  The rapid proliferation of encrypted network communication has significantly strengthened data privacy, yet it has simultaneously limited the effectiveness of traditional intrusion detection systems. Zero-day attacks, characterized by previously unseen signatu…
  crossref Anushrut Ghimire 2026-04-20T15:06:05Z 置信度 0.70
• AI bisa makin pintar tanpa mengintip data pribadi kita: Mengenal ‘federated learning’
  crossref Rachmad Atmoko 2026-01-19T08:11:25Z 置信度 0.70
• Defense Strategies for Federated Learning Models Targeted by Data Poisoning Attacks
  The chapter offers a detailed analysis of the security issues of Federated Learning (FL) and will particularly dwell on data poisoning attacks. It discusses the nature of decentralized architecture of FL, which results in exposing new attack surfaces despite m…
  crossref Monika Kumari, Nikhil Kumar Goyal, Ayesha Farooqi 2026-02-20T15:26:17Z 置信度 0.70
• Federated Learning for Real-Time Disease Prediction: A Scalable Framework for Personalized Healthcare in Internet of Things-Enabled Environment
  crossref K. Balamurugan, T.P. Latchoumi, Latha Parthiban, A. Venkateswara 等 2026-05-08T21:41:08Z 置信度 0.70
• Recommend Locally, Diversify Globally: FedFlex for Live Federated Netflix Recommendations
  crossref Sven Lankester, Gustavo de Carvalho Bertoli, Matias Vizcaino, Emma Beauxis-Aussalet 等 2026-07-29T19:14:00Z 置信度 0.70
• Federated Learning for Malware Image Classification under Data Heterogeneity
  crossref Victor Taiwo, Cemal Nişan, Muhammad Athallah, M. Gürsoy 等 2026-07-20T08:00:39Z 置信度 0.70
• FedPGP: Prototype-Guided Gradient Projection for Non-IID Federated Learning
  Federated learning (FL) provides an effective framework for multiple clients to jointly train neural networks without sharing their raw data. It has been widely used in privacy-preserving machine learning and edge intelligence. However, the highly non-independ…
  crossref Chuntong Liu, Jinmei Fan, Yanhai Zhang 2026-08-11T18:01:49Z 置信度 0.70
• Dual-LR: Learning Shared Transformation Knowledge through Additive Low-rank knowledge decomposition for Personalized Federated Learning
  Personalized Federated Learning (PFL) balances cross-client knowledge exchange and individual model adaptation amid non-uniform client data. Current PFL methods separate global and local knowledge via exclusive client-only personalization modules, yet they wro…
  crossref Dapeng Yan, Jie Kong, Yongjun Li 2026-07-13T00:34:29Z 置信度 0.70
• Collaboration Business Models and Frameworks in Federated Learning
  Federated Learning (FL) has enabled decentralised collaboration so that many stakeholders can train models without sharing raw data. However, the distributed nature exposes the system to risks such as data poisoning and unfavourable AI attacks, which compromis…
  crossref Revati Ramrao Rautrao, A. V. Senthil Kumar, Sanjayan Thottapattunjalil Suseelan 2026-02-20T15:26:17Z 置信度 0.70
• Suspicious Login Detection Using Machine Learning Under a Federated Learning Framework
  crossref P J Prajanya Jain, Shabari Shedthi B 2026-03-31T19:49:27Z 置信度 0.70
• Toward a Federated Organizational Intelligence Capability Model: Cross-Silo Federated Learning as a Distributed Dynamic Capability
  In response to the growing need for coordinated intelligence in highly regulated and data-fragmented environments, this study develops a Federated Organizational Intelligence Capability Model that conceptualizes cross-silo federated learning as a distributed o…
  crossref Avgousta Kyriakidou-Zacharoudiou, Elena Tsappi, Michael Georgiades 2026-05-13T16:26:37Z 置信度 0.70
• Wireless Federated Learning : Efficient Communication and Resource Management
  crossref Chung-Hsuan Hu 2025-12-08T22:01:59Z 置信度 0.70
• FedGIA: Personalized Federated Learning via Graph-based Invariant Aggregation
  crossref Shixing Leng, Qian Liu 2026-07-14T19:38:09Z 置信度 0.70
• Hybrid Federated Learning Framework for APT Attack Chain Detection and Privacy Enhancement
  To address the issues of cross-domain data privacy protection and collaborative analysis in the detection of Advanced Persistent Threat (APT) attack chains, this paper proposes an APT attack collaborative detection and privacy enhancement method based on hybri…
  crossref Jie Ji, Shi Qiu, Shengpeng Ye, Xin Liu 2026-08-04T09:04:33Z 置信度 0.70
• Privacy-Preserving Multimodal News Recommendation through Federated Learning
  crossref Mehdi Khalaj, Shahrzad Golestani Najafabadi, Julita Vassileva 2026-05-06T19:38:02Z 置信度 0.70
• Federated Learning in Health Care Technology
  crossref 2025-10-31T19:42:35Z 置信度 0.70
• DynaFed: A Dynamic Federated Learning Framework for Privacy-Preserving Crop Disease Detection
  Although AI-driven plant disease detection has achieved notable success, practical implementation is hindered by scarce datasets, privacy constraints, and non-independent and identically distributed (non-IID) data across production systems, which limit scalabi…
  crossref Mike O. Ojo, Azlan Zahid 2026-02-18T14:32:00Z 置信度 0.70
• Communication-Efficient and Privacy-Preserving Cooperative Robot-Arm Imitation Learning via Parallel Split Federated Learning
  crossref Shohei Kamiguchi, Takayuki Nishio 2026-06-29T19:38:15Z 置信度 0.70
• Deep Reinforcement Learning for Automated Hyperparameter Optimization in Multi-Institutional Federated Learning Frameworks
  crossref Dawakit Lepcha, Kanchan Thakur 2026-07-01T19:35:13Z 置信度 0.70
• Agricultural Supply Chain Using Federated Learning
  crossref 2026-06-23T12:51:06Z 置信度 0.70
• Fair and Robust Federated Learning Via Evolutionary Incentives
  crossref Fatemeh Shabani, Rasool Esmaeilyfard 2026-05-15T03:06:29Z 置信度 0.70
• A privacy-aware federated learning framework for collaborative healthcare IoT security
  crossref Rihab Saidi, Tarek Moulahi, Salah Zidi, Sami Mahfoudhi 2026-06-30T20:52:20Z 置信度 0.70
• Federated learning, explainability, and the road ahead
  crossref Harpreet Kaur, Archana Chhabra, Deepika Ghai 2026-03-27T09:13:04Z 置信度 0.70
• Differential Privacy in Quantum Federated Learning
  crossref Chaemoon Im, Soohyun Park, Joongheon Kim 2026-06-29T19:38:15Z 置信度 0.70
• Federated learning with clustered hyperparameter optimization
  crossref Wesley Chorney, Haifeng Wang, Sescu Adrian 2025-11-29T15:53:37Z 置信度 0.70
• Federated Learning for Privacy-Preserving AI-Based Medical Imaging in Cardiovascular, Neurological, and Oncology Applications
  The convergence of artificial intelligence and medical imaging has unlocked unprecedented diagnostic capabilities across cardiovascular, neurological, and oncological domains. Yet the aggregation of sensitive patient data remains constrained by stringent priva…
  crossref Yibo Kong 2026-07-29T14:11:21Z 置信度 0.70
• Federated Learning for Precision Medicine: A Blockchain-Enhanced Framework for Privacy-Preserving Predictive Analytics in Healthcare 5.0
  crossref Raj Kishor Verma 2026-02-06T05:46:59Z 置信度 0.70
• A Privacy-Preserving Learning Framework Based on Federated Learning and Explainable AI
  crossref Lili Xu, Li Li 2026-07-22T19:20:01Z 置信度 0.70
• Decision letter for "Breaking Interprovincial Data Silos: How Federated Learning Can Unlock Canada’s Public Health Potential"
  crossref 2026-03-14T21:10:36Z 置信度 0.70
• Decentralized Depression Detection Using Wearable Sensors and Federated Learning
  The symptoms that define depressive conditions have been recognized for millennia of medical history. The earliest Hippocratic writings not only define depression in similar ways as current works but also use context to differentiate ordinary sadness from depr…
  crossref Chandan Dhiman, Poonam Joyti, Mohit Kumar, Anshaj Chauhan 2026-04-23T17:45:47Z 置信度 0.70
• CIPHER-Fed: An Inference-Resistant Federated Learning Framework via StructuralGradient Obfuscation
  Federated Learning (FL) enables collaborative model training without sharing raw data, yet exchanged gradients remain vulnerable to inference and reconstruction attacks. Existing privacy-preserving methods typically protect gradient values, encrypt updates, or…
  crossref Waheeb Algethami, Guowei Wu, Faisal Alshami 2026-07-06T19:13:23Z 置信度 0.70
• Privacy-Preserving Multimodal Federated Learning with Reliability-aware Client Selection for Heterogeneous Edge Devices
  Federated learning is rapidly becoming a key paradigm for deploying machine learning models across distributed edge devices without aggregating raw data to a central server to satisfy emerging regulatory and ethical demands for data privacy. The extension to m…
  crossref Sai Doondi Kothapalli 2026-08-29T09:10:21Z 置信度 0.70
• Fair incentive allocation in vertical federated learning using nucleolus
  crossref Afsana Khan, Marijn ten Thij, Guangzhi Tang, Frank Thuijsman 等 2026-01-18T14:07:17Z 置信度 0.70
• Intelligent Workforce Management in Healthcare 5.0: Redefining HR Through Federated Learning
  crossref Archana Singh, Girish Lakhera, jyoti kumari, Arvind Nain 2026-02-06T05:46:45Z 置信度 0.70
• Federated Learning with Smartphone Sensor Data
  Federated learning (FL) is emerging as a promising approach for training machine learning models on distributed devices without violating the data privacy of these devices. In this paper, we examine federated learning for smartphone sensor data applications th…
  crossref Dheeraj Vaddepally 2026-01-08T07:19:47Z 置信度 0.70
• Conflict-Averse Federated Multi-Task Learning for Power System Stability Assessment
  Machine learning-based power system stability assessment has attracted significant research attention in recent years. However, most of the existing approaches treat pre-fault and post-fault assessment separately, missing opportunities to exploit their shared …
  crossref Renyou Xie, Rui Zhang, Chao Ren 2026-03-05T00:38:50Z 置信度 0.70
• Open-Source Federated Learning Platform Adapted for DICOM Medical Image Processing: Application to Breast Cancer Detection
  crossref Gersain Galández Buitrón, Juan Andrés Salazar-González, Jose Alejandro Salazar-Castro, Edwin Castillo 等 2026-07-29T19:12:10Z 置信度 0.70
• Federated Learning for Decentralized Healthcare: Privacy, Efficiency, and Scalability in Healthcare 5.0
  crossref Subrata Paul, Anirban Mitra, Shivnath Ghosh, Amitava Podder 2026-02-06T05:46:36Z 置信度 0.70
• Federated Learning in Healthcare Finance: A Systematic Review of Privacy-Preserving Models
  crossref Manika Garg, Sunitaa Tank, Bharat Kumar Tank 2026-02-06T05:46:47Z 置信度 0.70
• Federated Academic Risk Intelligence: Privacy-Preserving Multitask Learning with Stable Explanations Across Heterogeneous Educational Ecosystems
  Data-intensive learning settings provide new prospects for early academic-risk identification. However, institutional use of predictive analytics faces challenges related to data-governance requirements, diverse student populations, and the need for transparen…
  crossref Juling Niu 2026-08-21T05:51:21Z 置信度 0.70
• Privacy Protection and Trusted Data Sharing Mechanism Driven by Federated Learning
  crossref Shuangyue Li 2026-02-25T08:04:41Z 置信度 0.70
• Federated Machine Learning Outperforms Centralized Machine Learning for Fraud Detection: Evidence From 284,807 Real-World Transactions [Abstract]
  Aim/Purpose This study investigates whether federated machine learning (FML) can match or exceed centralized machine learning (CML) performance for financial fraud detection while maintaining complete data privacy. Background Financial fraud detection systems …
  crossref Samuel Sambasivam 2026-07-18T22:06:06Z 置信度 0.70
• Designing Equity-Focused School Climates Through Federated Learning Approaches
  crossref Preetee Praveen Kumar 2026-03-31T19:49:27Z 置信度 0.70
• Federated Learning for Privacy-Preserving Healthcare Wearable Security
  The work demonstrates and performs a privacy-preserving multi-institutional multi-party end-to-end federated learning (FL) framework to detect security anomalies in multi-party data streams of healthcare wearables. The natural pipeline uses distributed non-ide…
  crossref Grace Shalini T., Pratham Shrivastav, Parthiv Gopa 2026-06-11T20:45:37Z 置信度 0.70
• GENOME-SHIELD,An Executable Governance and Anomaly-Gated Federated Learning Framework for Single-Cell Analysis
  Background and Objective: Federated learning limits direct pooling of biomedical records, but conventional workflows do not bind data-use authorization, adversarial update screening and reproducible evidence into one executable control path. This study evaluat…
  crossref Naresh Somara 2026-08-27T09:40:41Z 置信度 0.70
• 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 2025 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 2025 置信度 0.66 federated learningmachine learningdata analysissecure multiparty computationprivacy enhancing technology
• Perfectly-Private Analog Secure Aggregation in Federated Learning
  In federated learning, multiple parties train models locally and share their parameters with a central server, which aggregates them to update a global model. To address the risk of exposing sensitive data through local models, secure aggregation via secure mu…
  datacite Jaramillo-Velez, Delio, Rajput, Charul, Freij-Hollanti, Ragnar, Hollanti, Camilla 等 2025 置信度 0.66 Machine Learning (cs.LG)Information Theory (cs.IT)FOS: Computer and information sciencesFOS: Computer and information sciences68P30
• 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 2025 置信度 0.66 federated learningmachine learningdata analysissecure multiparty computationprivacy enhancing technology
• D2.1 Privacy Preserving Feature Selection, Classificationand Federated Learning
  The HARPOCRATES project aims to develop solutions for private and secure data analysis and sharing. This deliverable (D2.1 Privacy Preserving Feature Selection, Classification and Federated Learning) presents the outcomes of research conducted in Work Package …
  datacite Iacovazzi, Alfonso, Eklund, David, Pyrgelis, Apostolos, Wang, Han 等 2025 置信度 0.66
• D2.1 Privacy Preserving Feature Selection, Classificationand Federated Learning
  The HARPOCRATES project aims to develop solutions for private and secure data analysis and sharing. This deliverable (D2.1 Privacy Preserving Feature Selection, Classification and Federated Learning) presents the outcomes of research conducted in Work Package …
  datacite Iacovazzi, Alfonso, Eklund, David, Pyrgelis, Apostolos, Wang, Han 等 2025 置信度 0.66