隐私计算与数据要素 · 全部实体（第 34 / 100 页）

本页收录第 3301–3400 条，共 10000 条 · 返回 隐私计算与数据要素 首页

• Murmura: A Framework for Federated and Decentralized Machine Learning
  Murmura is a comprehensive framework for federated and decentralized machine learning. Built for researchers and developers, it provides tools for distributed machine learning simulation with advanced privacy guarantees and flexible network topologies. The fra…
  datacite Rangwala, Murtaza 2026 置信度 0.66 federated learningdecentralized learningdistributed computingprivacy-preserving machine learningdifferential privacy
• Murmura: A Framework for Federated and Decentralized Machine Learning
  Murmura is a comprehensive framework for federated and decentralized machine learning. Built for researchers and developers, it provides tools for distributed machine learning simulation with advanced privacy guarantees and flexible network topologies. The fra…
  datacite Rangwala, Murtaza 2026 置信度 0.66 federated learningdecentralized learningdistributed computingprivacy-preserving machine learningdifferential privacy
• Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devices
  Continuous electrocardiography (ECG) monitoring could surface rhythm abnormalities before they escalate into cardiovascular events. However, a deployable system must satisfy three requirements simultaneously: legal-grade privacy (GDPR, HIPAA), real-time infere…
  datacite Akyol, Kaan Arda, Szeląg, Jakub Kacper, Abadi, Aydin, Alghamdi, Maha 等 2026 置信度 0.66 Cryptography and Security (cs.CR)Artificial Intelligence (cs.AI)Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• GreenFLag: A Green Agentic Approach for Energy-Efficient Federated Learning
  Progressing toward a new generation of mobile networks, a clear focus on integrating distributed intelligence across the system is observed to drive performance, autonomy, and real-time adaptability. Federated learning (FL) stands out as a key emerging techniq…
  datacite Panagea, Theodora, Koursioumpas, Nikolaos, Magoula, Lina, Khalili, Ramin 2026 置信度 0.66 Networking and Internet Architecture (cs.NI)FOS: Computer and information sciencesFOS: Computer and information sciences
• Secure Aggregation with Top-K Sparsification in Decentralized Federated Learning
  Secure aggregation is a vital component for mitigating gradient leakage in federated learning, but its communication cost conventionally scales with the gradient dimension. This becomes prohibitive for large models and even more pronounced in decentralized fed…
  datacite Tang, Hengxuan, Zhu, Jinbao, Tang, Xiaohu 2026 置信度 0.66 Information Theory (cs.IT)Cryptography and Security (cs.CR)Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• Compositional Generative Modeling from Decentralized Data
  Learning the compositional nature of the physical world requires joint observation of interacting factors. However, because practical data is often decentralized, these factors are fragmented across isolated silos. Existing decentralized generative approaches …
  datacite Morshed, Mashrur M., Boddeti, Vishnu Naresh 2026 置信度 0.66 Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• QSplitFL: Capability Aware Deep Q-Learning for Optimal Split Point Selection in Split Federated Learning
  Federated Learning (FL) combined with Split Learning (SL) is a privacy preserving paradigm that enables training deep neural networks (DNNs) on resource constrained devices while reducing overall training cost. However, determining the optimal split point, mea…
  datacite Shadin, Nazmus Shakib, Zhang, Xinyue, Wang, Jingyi, Pan, Miao 2026 置信度 0.66 Machine Learning (cs.LG)Artificial Intelligence (cs.AI)Cryptography and Security (cs.CR)FOS: Computer and information sciencesFOS: Computer and information sciences
• EndoDecay-Sim: A Hybrid In-Silico Digital Twin Platform for Cardiorenal Multimorbidity Tracking with Homomorphic Encryption
  🌐 LIVE INTERACTIVE DASHBOARD PLATFORM: Researchers and peer-reviewers can access and stress-test the live, cloud-deployed clinical decision support interface directly via: https://endodecay-sim-app-jp4wufjjemvbsnxxyj9xev.streamlit.app/ (Complete local deploym…
  datacite Zavrak, Muhammet Yagiz 2026 置信度 0.66
• EndoDecay-Sim: A Hybrid Computational Architecture for Stochastic Endothelial Degradation and Prognostic Multimorbidity Risk Stratification
  Release Notes and Academic Evaluation (v13.0-Ultimate) This repository marks the definitive evolution of EndoDecay-Sim into an integrated open-science computational medicine suite (v13.0). This version seamlessly bridges a non-linear Milstein Stochastic Differ…
  datacite Zavrak, Muhammet Yagiz 2026 置信度 0.66
• Federated Large Language Models: Current Progress and Future Directions
  Large Language Models have achieved impressive performance across diverse applications, yet their training typically depends on centralized data collection, raising serious privacy and governance concerns. Federated Learning offers a decentralized alternative …
  datacite Yao, Yuhang, Zhang, Jianyi, Wu, Junda, Huang, Chengkai 等 2024 置信度 0.66 Machine Learning (cs.LG)Computation and Language (cs.CL)FOS: Computer and information sciencesFOS: Computer and information sciences
• Model Poisoning Against Federated Model Adaptation with Chain of Bit-Flips
  Federated Learning (FL) allows a set of clients to collectively train a global model without sharing local training data. Giving the responsibility of the training to decentralized actors may lead to poisoning attacks: clients controlled by malicious third par…
  datacite Vuillod, Bastien, Hector, Kevin, Moellic, Pierre-Alain, Dutertre, Jean-Max 等 2026 置信度 0.66 Cryptography and Security (cs.CR)Artificial Intelligence (cs.AI)FOS: Computer and information sciencesFOS: Computer and information sciences
• Trustworthy Smart Fabs via Professional Proxies: Scaling Safe and Sustainable by Design (SSbD) through Industrial Data Spaces
  The convergence of the 2026 European Union Safe and Sustainable by Design (SSbD) framework, Corporate Sustainability Due Diligence Directive (CSDDD), and Carbon Border Adjustment Mechanism (CBAM) introduce a severe governance bottleneck for advanced semiconduc…
  datacite Liao, Han-Teng, Kao, Chang-Yi, Ang, Karen 2026 置信度 0.66 Cryptography and Security (cs.CR)Artificial Intelligence (cs.AI)Computational Engineering, Finance, and Science (cs.CE)Computers and Society (cs.CY)Human-Computer Interaction (cs.HC)
• AI-Native Closed-Loop Security for 6G-Enabled Cyber-Physical Systems: From Edge Detection to Network-Wide Mitigation
  In sixth-generation (6G) networks, billions of cyber-physical systems (CPSs) - autonomous vehicles, smart grids, industrial robots, and remote-surgical equipment - will run over ultra-reliable low-latency slices, collapsing the gap between a remote breach and …
  datacite Hussain, Bilal, Bilal, Muhammad, Li, Tan, Pervaiz, Haris 等 2026 置信度 0.66 Cryptography and Security (cs.CR)Machine Learning (cs.LG)Networking and Internet Architecture (cs.NI)FOS: Computer and information sciencesFOS: Computer and information sciences
• Amortizing Federated Adaptation: Hypernetwork Driven LoRA for Personalized Foundation Models
  Federated fine-tuning of foundation models using Low-Rank Adaptation (LoRA) offers a communication efficient solution for distributed learning. However, existing federated LoRA methods suffer from two fundamental limitations: (1) structural aggregation bias, w…
  datacite Gupta, Sunny, Shanker, Shambhavi, Sethi, Amit 2026 置信度 0.66 Artificial Intelligence (cs.AI)FOS: Computer and information sciencesFOS: Computer and information sciencesI.2.6; I.2.11; C.2.4; I.4; I.5.168T05, 68T07, 68W15, 65F55
• FedMental: Evaluating Federated Learning for Mental Health Detection from Social Media Data
  Social media text data are often used to train Machine Learning (ML) models to identify users exhibiting high-risk mental health behaviors. However, sharing this sensitive data poses privacy risks and limits the growth of benchmark datasets. We comprehensively…
  datacite Abdelkadir, Nuredin Ali, Ratnam, Anjali, Talat, Zeerak, Chancellor, Stevie 2026 置信度 0.66 Machine Learning (cs.LG)Computation and Language (cs.CL)FOS: Computer and information sciencesFOS: Computer and information sciences
• The Human Sovereignty Technology Paradigm — Supplement Version 2.0: Extended Prior Art Declaration
  Supplement Version 2.0 to the Human Sovereignty Technology Paradigm white paper (Version 1.0, March 29, 2026 �� zenodo.org/records/19323816). Establishes irrevocable public domain prior art for the Human Sovereignty Technology Platform (USPTO Provisional Appli…
  datacite Kershner, Braydon Alexander 2026 置信度 0.66 operator-bound technologyhuman sovereigntydata sovereigntydata destructionprior art
• The Human Sovereignty Technology Paradigm — Supplement Version 2.0: Extended Prior Art Declaration
  Supplement Version 2.0 to the Human Sovereignty Technology Paradigm white paper (Version 1.0, March 29, 2026 — zenodo.org/records/19323816). Establishes irrevocable public domain prior art for the Human Sovereignty Technology Platform (USPTO Provisional Applic…
  datacite Kershner, Braydon Alexander 2026 置信度 0.66 operator-bound technologyhuman sovereigntydata sovereigntydata destructionprior art
• Blockchain Solution with Artificial Intelligence Integration in the Indian Judicial System A Bibliometric and Methodical Literature Review
  The Indian judicial system faces critical challenges including case backlogs exceeding 54.7 million pending matters, insufficient judicial resources, and inefficient evidence management processes. This literature review examines the emerging potential of integ…
  datacite Sameer Patil, Darshana Desai 2026 置信度 0.66 BlockchainArtificial IntelligenceJudicial SystemLegal TechnologyIndia
• FedMTFI: Feature Importance Based Optimized Multi Teacher Knowledge Distillation in Heterogeneous Federated Learning Environment
  Federated learning (FL) is a decentralized approach that enables collaborative model training without exposing raw data. Instead of transferring sensitive data, it allows devices to share only model weights, keeping personal data locally and secure. However, i…
  datacite Shadin, Nazmus Shakib, Cummings, Aaron, Zhang, Xinyue, Deng, Bobin 2026 置信度 0.66 Machine Learning (cs.LG)Artificial Intelligence (cs.AI)FOS: Computer and information sciencesFOS: Computer and information sciences
• Pre-Registered Multi-Dataset Validation of Three-Way Composition III Privacy-Preserving Architecture for Musculoskeletal-Kinematic Clinical Digital Twins
  v2 update (2026-06-01): Reproducibility ZIP added back to the latest version alongside the manuscript files, so that downloading from the concept DOI gives all materials in one place rather than requiring navigation to v1. This reproducibility archive accompan…
  datacite Ferlic, Randolph James, Ferlic, Kimberly Kate 2026 置信度 0.66 pre-registrationfederated learningdifferential privacymembership inferencemusculoskeletal kinematics
• The Gaussian-Head OFL Family: One-Shot Federated Learning from Client Global Statistics
  Classical Federated Learning relies on a multi-round iterative process of model exchange and aggregation between server and clients, with high communication costs and privacy risks from repeated model transmissions. In contrast, one-shot federated learning (OF…
  datacite Turazza, Fabio, Picone, Marco, Mamei, Marco 2026 置信度 0.66 Machine Learning (cs.LG)Artificial Intelligence (cs.AI)FOS: Computer and information sciencesFOS: Computer and information sciences
• FEDL: A Federated Continual Learning Framework for Lifelong Adaptation in Non-Stationary Wireless Sensor Networks
  Deploying deep learning in Wireless Sensor Networks (WSNs) is challenging in non-stationary environments, where concept drift causes models to become outdated. Although ((FL) saves communication, it forgets old knowledge. Although Continual Learning (CL) preve…
  crossref Vijayakumar K, Thirumaraiselvan P 2026-02-04T16:42:33Z 置信度 0.70
• Communication-Efficient Federated Transfer Learning with Differential Privacy
  crossref Yushi Liu 2026-08-26T19:11:55Z 置信度 0.70
• Blockchain Integration to Enhance Federated Learning Model Integrity
  Federated Learning is a distributed machine learning approach that enables model training without transferring raw data, thereby preserving user privacy. To improve conciseness, overlapping explanations of FL’s privacy benefits across the Abstract, Introductio…
  crossref Yane Devi Anna 2026-03-09T04:42:14Z 置信度 0.70
• Metric-Guided Attribution Fusion in Explainable Federated Learning: An Empirical Evaluation
  Federated Learning (FL) enables privacy-preserving model training, but the behavior of eXplainable Artificial Intelligence (XAI) techniques in FL remains insufficiently understood, leaving an open challenge for trustworthy federated systems.We analyze how fede…
  crossref Nicolas Schuler, Matteo Camilli, Vincenzo Scotti, Raffaela Mirandola 2026-07-16T21:44:34Z 置信度 0.70
• A Control-Variate Adaptive Weighting Algorithm for Cross-Dataset Federated Learning Motor Imagery EEG Decoding
  crossref Sufen Wang, Minmin Miao 2026-08-07T19:22:47Z 置信度 0.70
• The optimization of incentive mechanisms for edge federated learning based on Stackelberg game
  crossref Liping He 2026-05-23T04:18:00Z 置信度 0.70
• Bridging Local and Federated Data Normalization in Federated Learning: A Privacy-Preserving Approach
  crossref Melih Coşğun, Mert Gençtürk, Sinem Sav 2026-04-29T19:52:30Z 置信度 0.70
• From Fragmentation to Trust: Standardizing Communication in Federated Learning
  crossref Sridharan Sankaran 2026-04-16T19:50:24Z 置信度 0.70
• DACS: Deadline-Aware Adaptive Client Selection in federated learning
  crossref Aref Arefnia, Abdolah Chalechale 2026-07-03T12:12:00Z 置信度 0.70
• Federated learning for ICD classification with lightweight models and pretrained embeddings
  crossref Binbin Xu, Gerard Dray 2026-06-19T08:54:02Z 置信度 0.70
• HADAG: An Asynchronous DAG Provenance Architecture for  Federated Healthcare Learning under Byzantine and Network Stress
  Federated healthcare learning must accommodate heterogeneous communication and malicious participants while retaining a traceable history of asynchronous model updates. Existing work commonly studies these requirements separately. This paper presents HADAG, a …
  crossref David Gana, Anju Johnson 2026-08-15T06:39:31Z 置信度 0.70
• Cloud-Native Document Intelligence via Federated Multi-Agent Transformers
  crossref Ranadheer Reddy Charabuddi 2026-03-31T19:49:17Z 置信度 0.70
• Optimizing Federated Learning with Particle Swarm Intelligence for Next-Generation Video Surveillance
  crossref Salma Newegy, Bo Zhang 2026-03-10T19:50:57Z 置信度 0.70
• Let’s focus: Focused backdoor attack against federated transfer learning
  crossref Marco Arazzi, Stefanos Koffas, Antonino Nocera, Stjepan Picek 2026-05-21T06:50:33Z 置信度 0.70
• <p>TaxFL: Federated Learning and Federated Graph Intelligence for Cross-Border Tax Compliance – A privacy-enhancing blueprint for collaborative risk analytics without sharing raw taxpayer data</p>
  <p>Cross-border tax non-compliance and money laundering exploit fragmented data silos across jurisdictions. This working paper proposes TaxFL, a pragmatic privacy-preserving framework that enables tax authorities and FIUs to collaboratively train risk mo…
  crossref pedro frantz 2026-04-24T13:38:21Z 置信度 0.70
• LLMs meet Federated Learning for Scalable and Secure IoT Management
  crossref Yazan Otoum, Arghavan Asad, Amiya Nayak 2026-07-14T19:38:09Z 置信度 0.70
• Curvature-Aware Model Aggregation for Decentralized Federated Learning via Parallel Transport on Discrete Manifolds
  Abstract Decentralized federated learning (DFL) allows a group of distributed users to train a shared model without relying on a central server. Most existing decentralized aggregation methods, however, still treat the communication network as a flat Euclidean…
  crossref Wei Gao 2026-07-01T03:18:06Z 置信度 0.70
• AS-FL: Accumulator and Signature-Based Federated Learning with Weight Aggregation
  crossref Runtong Xu 2026-07-28T19:08:01Z 置信度 0.70
• Hierarchical Asynchronous Federated Learning over Space-Air-Ground Integrated Networks
  crossref Kai Wang, Chee Wei Tan 2026-06-16T19:41:29Z 置信度 0.70
• Federated Learning: A Paradigm Shift in Healthcare Data Privacy
  crossref Anjuli Goel, Chander Prabha 2026-01-02T02:20:47Z 置信度 0.70
• FLBL2: Real-World Layer-2 Blockchain Auditing for Federated Learning on Edge Hardware
  Federated learning (FL) enables privacy-preserving collaborative trainingacross distributed edge devices, yet each aggregation round produces noexternally verifiable record, so the training history cannot be independentlyaudited after the fact. This accountabi…
  crossref Talgar Bayan, Bibarys Mukhambetiyar, Adnan Yazıcı 2026-07-06T14:39:44Z 置信度 0.70
• Privacy-Preserving Clustered Federated Learning Against Membership Inference Attacks
  crossref Yangjing Shi 2026-06-16T19:41:35Z 置信度 0.70
• Privacy-Preserving Federated Learning for Medical Imaging with Uncertainty Estimation: A Novel Framework for Collaborative Healthcare AI
  crossref Prince Kumar 2026-01-12T22:28:46Z 置信度 0.70
• LiteTalk: Communication-Efficient Federated Learning via Adaptive Gradient Drift Detection
  crossref Vahideh Hayyolalam, Öznur Özkasap 2026-07-14T19:38:09Z 置信度 0.70
• FEDL: Algorithmic Co-design of Federated and Continual Learning for Resource-Constrained Non-Stationary Environments
  We propose FEDL, a novel federated continual learning algorithmic framework designed for resource-constrained, non-stationary environments. Its core algorithmic innovations include the following: (1) a lightweight, event-driven delta detector that triggers lea…
  crossref Vijayakumar K, Thirumaraiselvan P 2026-03-26T13:41:42Z 置信度 0.70
• FedDBA: Federated learning based image classification algorithm with local bias-contrastive learning
  crossref Jin Ye, Huilin Hu, Junbin Liang 2026-05-25T16:12:15Z 置信度 0.70
• Federated Edge Learning
  crossref Yong Zhou, Wenzhi Fang, Yuanming Shi, Khaled B. Letaief 2025-08-29T12:52:18Z 置信度 0.70
• GIDD: Gradient inversion using diffusion model for denoising in federated learning
  crossref Xuebo Wang, Hongguang Sun, Yi He 2026-01-20T07:42:18Z 置信度 0.70
• Improving Generalization with Harmonic Aggregation in Personalized Federated Learning
  crossref Angel Peredo, Sergei Chuprov 2026-02-04T20:45:15Z 置信度 0.70
• Research on Privacy-Preserving AI Model Training and Validation Methods Based on Federated Learning
  crossref 2026-02-28T02:03:30Z 置信度 0.70
• Power to the Clients: Federated Learning in a Dictatorship Setting
  crossref Mohammadsajad Alipour, Mohammad Mohammadi Amiri 2026-08-25T19:17:35Z 置信度 0.70
• CASGuard: Collusion-Aware Soft Suppression for Defending Sybil Backdoor Attacks in Federated Learning
  Federated learning enables collaborative model training without directly sharing private data, but its distributed and partially observable training process exposes it to severe poisoning threats. Among them, Sybil backdoor attacks are particularly dangerous b…
  crossref Yaoyao Ren, Xu Cheng, Yu Cao 2026-06-10T20:48:02Z 置信度 0.70
• Explainable Hierarchical Federated Learning for Privacy-Preserving Clinical Decision Support in Primary Care
  Clinical Decision Support Systems (CDSS) are increasingly adopted interritorial healthcare to support the early detection and management ofchronic diseases. However, their deployment in real-world healthcareinfrastructures remains challenging due to data fragm…
  crossref Roberto Marino, Irene Cacciola, Massimo Villari 2026-08-04T13:24:21Z 置信度 0.70
• Causal graph based fairness auditing for federated learning under selection bias
  In federated learning, selection bias can distort fairness assessments and mask or mimic algorithmic discrimination. This study proposes a fairness auditing framework that integrates causal graph modeling with statistical parity metrics to audit discrimination…
  crossref Haoyu Xue, Xiaohang Zhang, Zhengren Li, Fei Chen 2026-06-24T12:42:47Z 置信度 0.70
• Federated learning applications for IoMT security
  crossref Priyadharshini M., Murugesh V., Sam Kumar G. V., Subrata Chowdhury 2026-04-08T21:46:59Z 置信度 0.70
• Defence Against Byzantine Attacks in Semi-Supervised Federated Learning
  crossref Nafisa Parvin, Sayanta Sen, Saumik Bhattacharya 2026-08-13T19:11:15Z 置信度 0.70
• Diverse Embedding Graph Attention Networks for Personalized Federated Learning
  crossref Xiao Chen 2026-08-13T19:16:59Z 置信度 0.70
• Blockchain and Federated Learning Synergy for Privacy-Focused DeepFex Solutions
  crossref M. Harishmaa, S. Janani, K. A. Jayashree, J. Rufina Sherin 等 2025-11-07T13:28:15Z 置信度 0.70
• FedPSS: Privacy-governed federated patient-record similarity retrieval over temporal multimodal clinical records using multilingual semantic mapping
  crossref Mohammad Tanhaei 2026-06-19T15:57:14Z 置信度 0.70
• Privacy Preservation Through Design-Federated Learning Architectures for Secure and Immersive Metaverse-Based Healthcare
  Crossover of the metaverse, Artificial Intelligence, and Federated Learning is changing the healthcare systems and amplifying the issues of privacy, security, and trust. This chapter discusses privacy preservation by design as a fundamental concept to applying…
  crossref S. Aarthi, Jaypalsinh A. Gohil 2026-05-06T18:23:51Z 置信度 0.70
• Marketing Analytics Without Personal Identifiers: Federated Learning And DP
  The study is devoted to the analysis and conceptual integration of two key classes of privacy-enhancing technologies (PETs), federated learning (FL) and differential privacy (DP), with the aim of developing a holistic framework for solving marketing analytics …
  crossref Kuanysh Kemeshova 2026-02-12T09:38:34Z 置信度 0.70
• Incremental Federated Learning for Intrusion Detection in IoT Networks under Evolving Threat Landscape
  crossref Muaan Rehman, Hayretdin Bahsi, Rajesh Kalakoti 2026-03-08T11:24:37Z 置信度 0.70
• Federated Learning and Deep Reinforcement Learning for Maintenance-Aware Energy Optimization in Industrial Wireless Sensor Networks
  crossref Tanguturi Tejasree, Shaik Ghouhar Taj 2026-05-26T19:40:12Z 置信度 0.70
• Centralized Versus Decentralized Federated Learning Architectures: Design Trade-Offs, Security, and Performance in Healthcare 5.0 Applications
  crossref Zakariae Saidi, Ouidad Akhrif, Younes El Bouzekri El Idrissi 2026-02-06T05:46:42Z 置信度 0.70
• Privacy-preserving federated learning approach for medical image analysis
  crossref Zishuo Xu 2026-04-29T18:42:01Z 置信度 0.70
• Scaling Limits of Federated Learning for Road Damage Detection Across Countries
  Federated learning enables cross-country collaboration for training vision-based road damage detection models without sharing raw data. However, empirical guidance on federation configuration and client composition remains limited. Specifically, the effects of…
  crossref Shubham Kumar Dwivedi, Deeksha Arya, Yoshihide Sekimoto 2026-02-17T01:37:36Z 置信度 0.70
• Trustless Federated Reinforcement Learning for VPP Dispatch
  Large-scale Virtual Power Plants (VPPs) are increasingly essential as Distributed Energy Resources (DERs) assume ancillary service duties once supplied by conventional generation, yet scaling a VPP exposes a persistent trilemma among economic efficiency, data …
  crossref Xin Zhang, Fan Liang 2026-03-20T10:49:58Z 置信度 0.70
• Federated Learning- Based 3D Medical Image Compression
  crossref 2026-04-22T05:09:37Z 置信度 0.70
• A method for predicting electrolytic aluminum temperature based on machine learning and federated learning
  crossref Junyi Yu, Jiajia Xu 2026-03-24T19:46:39Z 置信度 0.70
• Blockchain-Enabled Adaptive Federated Learning Framework with SHAP-Based Explainability for Financial Fraud Detection
  crossref Junaid Hussain, Aatira Anum 2026-01-05T21:40:13Z 置信度 0.70
• Physics-Informed Federated Learning for Decentralized Pharmaceutical Crystallization: Achieving Personalized Predictive Accuracy with Minimal Data
  In decentralized pharmaceutical manufacturing, developing robust predictive models for crystallization is often hindered by proprietary data silos and the inherent heterogeneity of site-specific process dynamics. Standard machine learning approaches struggle w…
  crossref Sai Vinay Thattukolla 2026-05-06T14:43:11Z 置信度 0.70
• EMERGING LEARNING PARADIGMS IN MACHINE LEARNING: TRANSFER, META, AND FEDERATED APPROACHES
  There have been recent breakthroughs in machine learning which have given rise to new learning paradigms beyond traditional single-task, centralized training. Three of these paradigms (transfer learning, meta-learning and federated learning) are designed to en…
  crossref Nidhi Bhavsar, Kriti Das, Apeksha Waghmare, Komal Dhule 2026-07-03T10:12:35Z 置信度 0.70
• Federated Learning Driven Machine Learning Models for Network Intrusion Detection
  crossref J.Saira Banu, Sumaiya Thaseen Ikram, Harpal Kaur Dhindsa 2026-07-08T15:38:55Z 置信度 0.70
• Semi-asynchronous Federated Learning in Flower: Framework Extension and Performance Assessment
  This paper presents an extension of the Flower federated learning framework to support Semi-Asynchronous Federated Learning. The proposed approach adapts the traditional synchronous paradigm to better handle client heterogeneity and straggler effects. By intro…
  crossref Víctor Hidalgo-Izquierdo, Carmen Carrión, Blanca Caminero 2026-07-06T20:51:52Z 置信度 0.70
• Location Privacy Preservation Crowdsensing with Federated Reinforcement Learning
  This paper describes a new way to deal with privacy in crowdsensing through a federated reinforcement learning system. The goal of this system is to give certain people the ability to keep their location hidden when collecting information from them. A k-anonym…
  crossref Jothi Prasad V, Aditya S, Shalbin K Eldo, Shivashankaran S 2026-04-14T22:29:02Z 置信度 0.70
• A Federated Learning Approach for Link Prediction in Social Learning Networks with Varying Link Definitions
  crossref Jeremy Wong, Rajeev Sahay 2026-08-21T13:35:24Z 置信度 0.70
• Federated Deep Learning for Multi-Class Deepfake Detection Across Diverse Manipulations
  The exponential growth of digital media, particularly through social networks generating approximately 0.5 zettabytes of data daily, has created unprecedented challenges in content authenticity verification. Advanced generative technologies including GANs, dif…
  crossref Pochampally Chandra Sekhar Reddy, Kongara Srinivasa Rao 2026-07-08T17:36:48Z 置信度 0.70
• Federated Deep Reinforcement Learning for Load Balancing in 6G Edge Networks
  The emergence of sixth-generation (6G) communication systems is poised to fundamentally transformelectronics by enabling ultra-reliable low latency communication (URLLC), terabit level data ratesand seamless connectivity across smart devices, wearables, immers…
  crossref Salman Khan, Woong-Kee Loh, Ikram Syed 2026-05-21T20:38:31Z 置信度 0.70
• Communication-Efficient and Privacy-Preserving Federated Learning for Serverless Intelligence in Infrastructure-Denied UAV Relay Swarms
  A UAV relay fleet operating where infrastructure has failed must learn collectively from what its aircraft observe, yet everything about its situation forbids the standard recipe: raw data cannot cross links an adversary may monitor, no ground server can be as…
  crossref Abdulmajid Kasai 2026-07-24T09:35:15Z 置信度 0.70
• Hierarchical Federated Reinforcement Learning for Collaborative Wireless Distributed Systems
  crossref Xiaoyang Zhang, Chen-Khong Tham 2026-06-16T19:41:29Z 置信度 0.70
• Communication-efficient Federated Learning via Feature Correlation and Entropy Analysis
  The FL system enables several clients to train a model without compromising their private data. However, FL has limitations in terms of communication overhead and model convergence due to different data distribution for different clients. To overcome this limi…
  crossref Amir mollanejad, nahideh Derakhshanfard, mihan hoseinnezhad, Abbas Mirzaei 2026-02-14T21:39:17Z 置信度 0.70
• Federated Learning for Privacy-Preserving Healthcare Data
  crossref 2026-07-28T07:13:15Z 置信度 0.70
• Design of Privacy Protection Mechanism for Federated Learning Oriented to Data Security
  crossref 2026-04-22T03:14:36Z 置信度 0.70
• Fed-TTC: A Pareto-Efficient Hierarchical Federated Learning Framework
  crossref Seyed Salar Ghazi 2026-06-16T19:42:37Z 置信度 0.70
• Noise-Resilient Quantum Federated Learning for NISQ Edge Computing: Physical Noise Modeling and Robust Convergence Optimization
  Quantum federated learning (QFL) promises dramatic communication and parameter efficiency for resource-constrained Internet of Things (IoT) edge networks by replacing classical deep neural networks with compact variational quantum circuits (VQCs). However, cur…
  crossref Sungkwan Youm 2026-04-07T15:43:13Z 置信度 0.70
• Multilingual Cultural Metaphor Transfer Driven by Federated Learning
  Traditional federated learning faces significant challenges in multilingual cultural metaphor transfer because nonindependent and identically distributed language data make it difficult to balance local cultural heterogeneity with global semantic consistency. …
  crossref L. Shi 2026-08-14T10:12:43Z 置信度 0.70
• FedQEdge: Joint Model Compression and Federated Learning Framework for Edge AI
  crossref Ramasubramanian Balasubramanian 2026-05-12T19:46:53Z 置信度 0.70
• Hybrid Federated Learning Architecture for Communication-Efficient D2D-Enabled 6G Networks
  crossref Mahima Atmakuri, Ajay Bhardwaj 2026-05-15T03:00:58Z 置信度 0.70
• Ontology-Based Data Harmonization and Federated Transfer Learning: Enabling Scalable and Interoperable Intelligence in Healthcare 5.0
  crossref Darapu Uma, Manas Kumar Yogi 2026-02-06T05:46:31Z 置信度 0.70
• E-TUNE-FL: Efficient and adapTive semi-synchronoUs semi-deceNtralizEd Federated Learning
  Deep learning (DL) has seen extensive adoption across a broad range of applications. In the context of network management, it plays a crucial role in the development of advanced Intrusion Detection Systems (IDS). However, the massive volumes of log data produc…
  crossref Houssem Jmal, Kandaraj Piamrat, Ons Aouedi 2026-02-17T19:41:47Z 置信度 0.70
• ODFU: Off-Diagonal Correlation-Aware federated unlearning for dropout attack defense in federated learning
  crossref Cao Qui, Le-Pham Hoang-Trung, Kim-Hung Le 2026-07-21T15:34:19Z 置信度 0.70
• Optimization of Artificial Intelligence Algorithms Based Federated Graph Learning
  crossref Yunbo Wang 2026-06-11T19:57:57Z 置信度 0.70
• Federated Learning for Privacy-Preserving Big Data Analytics in Distributed Systems
  Federated Learning (FL) is an important concept in big data analytics because it has changed the way collaborative model training can be done on devices that are decentralized while ensuring user privacy, an essential requirement in an accurate evidence-based …
  crossref Ahmed Gheni Dawood, Ekhlas Muthanna Turki 2026-06-09T02:13:59Z 置信度 0.70
• Secure Collaborative Learning for CSV-Based Ovarian Cancer Diagnosis: A Federated Approach
  crossref Bidita Sarkar Diba, Md. Arafat Kabir, Tasnim Jahin Mowla, Hanif Bhuiyan 等 2025-10-31T19:42:51Z 置信度 0.70
• Real-World Case Studies of Data Poisoning Attacks in Federated Learning Applications
  This chapter looks at real-world examples of data poisoning attacks in federated learning (FL) systems, where harmful participants change local data to hurt the accuracy or integrity of global models. It uses case studies from healthcare, finance, the Internet…
  crossref Shradha Sonawane, Gitanjali Shinde, Grishma Bobhate, Sonal Fatangare 等 2026-02-20T15:26:17Z 置信度 0.70
• ANCHOR: A Multilayer Reputation-Based Defense for Federated Learning in Edge-Fog-Cloud IoT Systems
  Multilayer federated learning (multilayer FL) improves scalability and communication efficiency in large-scale IoT networks. However, it remains vulnerable to poisoning attacks in which adversaries corrupt local training to bias the global model. Defense is pa…
  crossref Mumin Adam, Uthman Baroudi 2026-02-20T20:43:51Z 置信度 0.70
• STPS-NET: Federated Learning for Plant-Specific Spatio-Temporal Modeling in Urban Rooftop Agriculture
  Urban rooftop agriculture in tropical megacities has significant potential to improve food security and environmental sustainability. However, large-scale deployment remains constrained by three key challenges: strong microclimate variability across urban roof…
  crossref Ahad Bin Islam Shoeb 2026-03-26T23:55:31Z 置信度 0.70
• Bandwidth-Aware Decentralized Federated Learning in Wired Networks
  crossref Kengo Tajiri, Ryoichi Kawahara 2026-08-27T19:07:07Z 置信度 0.70
• Cotton leaf disease detection using federated learning
  crossref 2026-06-29T11:36:17Z 置信度 0.70