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• The MICCAI Federated Tumor Segmentation (FeTS) Challenge 2024: Efficient and Robust Aggregation Methods for Federated Learning
  We present the design and results of the MICCAI Federated Tumor Segmentation (FeTS) Challenge 2024, which focuses on federated learning (FL) for glioma sub-region segmentation in multi-parametric MRI and evaluates new weight aggregation methods aimed at improv…
  datacite Linardos, Akis, Pati, Sarthak, Baid, Ujjwal, Edwards, Brandon 等 2025 置信度 0.66 Computer Vision and Pattern Recognition (cs.CV)Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• A Rigorous Multidisciplinary Theoretical Framework for Synergistic Bio- and Non-Biochemical Interventions to Reverse Cellular and Tissue Aging: Quantitative Stochastic Modeling, Clinical Applications, and Translational Precision Medicine
  This pioneering theoretical framework integrates biochemistry, bioengineering, applied mathematics, computational biology, and pharmacology to model synergistic bio- and non-biochemical interventions for cellular and tissue rejuvenation, advancing beyond exist…
  datacite shibah, Sami Rashid Mohammed 2025 置信度 0.66
• A Rigorous Multidisciplinary Theoretical Framework for Synergistic Bio- and Non-Biochemical Interventions to Reverse Cellular and Tissue Aging: Quantitative Stochastic Modeling, Clinical Applications, and Translational Precision Medicine
  This comprehensive theoretical framework integrates biochemistry, bioengineering, applied mathematics, computational biology, and pharmacology to model synergistic bio- and non-biochemical interventions for cellular and tissue rejuvenation. We develop and rigo…
  datacite shibah, Sami Rashid Mohammed 2025 置信度 0.66
• THE ECONOMIC ARCHITECTURE OF THE METABOLIC AGE A Scientific, Policy, and Economic Valuation of the CollectiveOS Anti-Scarcity Stack
  THE ECONOMIC ARCHITECTURE OF THE METABOLIC AGE A Scientific, Policy, and Economic Valuation of the CollectiveOS Anti-Scarcity Stack Version 1.0 — Research Edition 1. Executive Summary Humanity currently stands at a precarious structural threshold, transitionin…
  datacite Brewer, Mark Anthony 2025 置信度 0.66
• THE ECONOMIC ARCHITECTURE OF THE METABOLIC AGE A Scientific, Policy, and Economic Valuation of the CollectiveOS Anti-Scarcity Stack
  THE ECONOMIC ARCHITECTURE OF THE METABOLIC AGE A Scientific, Policy, and Economic Valuation of the CollectiveOS Anti-Scarcity Stack Version 1.0 — Research Edition 1. Executive Summary Humanity currently stands at a precarious structural threshold, transitionin…
  datacite Brewer, Mark Anthony 2025 置信度 0.66
• Multi-Frequency Federated Learning for Human Activity Recognition Using Head-Worn Sensors
  Human Activity Recognition (HAR) benefits various application domains, including health and elderly care. Traditional HAR involves constructing pipelines reliant on centralized user data, which can pose privacy concerns as they necessitate the uploading of use…
  datacite Fenoglio, Dario, Li, Mohan, Casnici, Davide, Laporte, Matias 等 2025 置信度 0.66 Machine Learning (cs.LG)Distributed, Parallel, and Cluster Computing (cs.DC)FOS: Computer and information sciencesFOS: Computer and information sciences
• Federated Learning Survey: A Multi-Level Taxonomy of Aggregation Techniques, Experimental Insights, and Future Frontiers
  The integration of IoT and AI has unlocked innovation across industries, but growing privacy concerns and data isolation hinder progress. Traditional centralized ML struggles to overcome these challenges, which has led to the rise of Federated Learning (FL), a…
  datacite Arbaoui, Meriem, Brahmia, Mohamed-el-Amine, Rahmoun, Abdellatif, Zghal, Mourad 2025 置信度 0.66 Machine Learning (cs.LG)Distributed, Parallel, and Cluster Computing (cs.DC)FOS: Computer and information sciencesFOS: Computer and information sciences
• A Robust Federated Learning Approach for Combating Attacks Against IoT Systems Under non-IID Challenges
  In the context of the growing proliferation of user devices and the concurrent surge in data volumes, the complexities arising from the substantial increase in data have posed formidable challenges to conventional machine learning model training. Particularly,…
  datacite Gad, Eyad, Fadlullah, Zubair Md, Fouda, Mostafa M. 2025 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
• PROMETHEUS NEEDS AN OPERATING SYSTEM: Why Jeff Bezos' $6.2B Vision Requires the CollectiveOS Anti-Scarcity Stack
  PROMETHEUS NEEDS AN OPERATING SYSTEM: Why Jeff Bezos’ $6.2B Vision Requires the CollectiveOS Anti-Scarcity Stack 1. Introduction: The Limits of Promethean Thinking The mythological Prometheus stole fire from the gods to empower humanity, an act of rebellion th…
  datacite Brewer, Mark Anthony 2025 置信度 0.66
• PROMETHEUS NEEDS AN OPERATING SYSTEM: Why Jeff Bezos' $6.2B Vision Requires the CollectiveOS Anti-Scarcity Stack
  PROMETHEUS NEEDS AN OPERATING SYSTEM: Why Jeff Bezos’ $6.2B Vision Requires the CollectiveOS Anti-Scarcity Stack 1. Introduction: The Limits of Promethean Thinking The mythological Prometheus stole fire from the gods to empower humanity, an act of rebellion th…
  datacite Brewer, Mark Anthony 2025 置信度 0.66
• Trusted Research Environments for Healthcare AI: State of the Art Global Landscape Report
  This global landscape report provides a comprehensive overview of the current state of Trusted Research Environments (TREs) worldwide, with a particular focus on the Nordic countries and the United Kingdom. TREs—also known as data safe havens—are secure platfo…
  datacite Emanuilov, Ivo, Larsson, Björn, Dubber, Andrew, Magas, Michela 2025 置信度 0.66
• Trusted Research Environments for Healthcare AI: State of the Art Global Landscape Report
  This global landscape report provides a comprehensive overview of the current state of Trusted Research Environments (TREs) worldwide, with a particular focus on the Nordic countries and the United Kingdom. TREs—also known as data safe havens—are secure platfo…
  datacite Emanuilov, Ivo, Larsson, Björn, Dubber, Andrew, Magas, Michela 2025 置信度 0.66
• Planetary-Scale Autonomous Infrastructure: A Strategic Framework for Sovereign Sanctuary Deployment, Heritage Restoration, and Open-Science Ecosystems
  Planetary-Scale Autonomous Infrastructure: A Strategic Framework for Sovereign Sanctuary Deployment, Heritage Restoration, and Open-Science Ecosystems Executive Summary: The Architecture of the Inverted Colony The convergence of sixth-generation artificial int…
  datacite Brewer, Mark Anthony 2025 置信度 0.66
• Planetary-Scale Autonomous Infrastructure: A Strategic Framework for Sovereign Sanctuary Deployment, Heritage Restoration, and Open-Science Ecosystems
  Planetary-Scale Autonomous Infrastructure: A Strategic Framework for Sovereign Sanctuary Deployment, Heritage Restoration, and Open-Science Ecosystems Executive Summary: The Architecture of the Inverted Colony The convergence of sixth-generation artificial int…
  datacite Brewer, Mark Anthony 2025 置信度 0.66
• IMMORTAL TEK: The Sovereign Node — Bio-Sovereign Infrastructure & The Post-Silicon Paradigm (2025–2028)
  IMMORTAL TEK: The Sovereign Node — Bio-Sovereign Infrastructure & The Post-Silicon Paradigm (2025–2028) 1. Executive Summary: The Entropic Limit of Consumer Electronics The current trajectory of personal computing has reached a terminal velocity of diminishing…
  datacite Brewer, Mark Anthony 2025 置信度 0.66
• IMMORTAL TEK: The Sovereign Node — Bio-Sovereign Infrastructure & The Post-Silicon Paradigm (2025–2028)
  IMMORTAL TEK: The Sovereign Node — Bio-Sovereign Infrastructure & The Post-Silicon Paradigm (2025–2028) 1. Executive Summary: The Entropic Limit of Consumer Electronics The current trajectory of personal computing has reached a terminal velocity of diminishing…
  datacite Brewer, Mark Anthony 2025 置信度 0.66
• Federated Behavioural Planes: Explaining the Evolution of Client Behaviour in Federated Learning
  Federated Learning (FL), a privacy-aware approach in distributed deep learning environments, enables many clients to collaboratively train a model without sharing sensitive data, thereby reducing privacy risks. However, enabling human trust and control over FL…
  datacite Fenoglio, Dario, Dominici, Gabriele, Barbiero, Pietro, Tonda, Alberto 等 2024 置信度 0.66 Machine Learning (cs.LG)Distributed, Parallel, and Cluster Computing (cs.DC)FOS: Computer and information sciencesFOS: Computer and information sciences
• A Rigorous Multidisciplinary Theoretical Framework for Synergistic Bio- and Non-Biochemical Interventions to Reverse Cellular and Tissue Aging: Quantitative Stochastic Modeling, Clinical Applications, and Translational Precision Medicine
  This culminating theoretical framework synthesizes a multidisciplinary paradigm for synergistic bio- and non-biochemical interventions to rejuvenate cellular and tissue vitality. Integrating biochemistry, bioengineering, applied mathematics, computational biol…
  datacite shibah, Sami Rashid Mohammed 2025 置信度 0.66
• A Comprehensive Review of Data Privacy Challenges in Social Media Platforms
  Social media privacy has become one of the most urgent 21st-century digital concerns, as sites frame communication, identity, and public discourse while also facilitating surveillance, profiling, and exploitation. This review examines twenty peer-reviewed arti…
  datacite Manikantan, R, Meghana, J, Padmavathi, C 2025 置信度 0.66 Privacy Risks and ChallengesUser behavior and awarenessGDPR and Data ProtectionLegal and Regulatory FrameworksSocial Media Platforms
• A Comprehensive Review of Data Privacy Challenges in Social Media Platforms
  Social media privacy has become one of the most urgent 21st-century digital concerns, as sites frame communication, identity, and public discourse while also facilitating surveillance, profiling, and exploitation. This review examines twenty peer-reviewed arti…
  datacite Manikantan, R, Meghana, J, Padmavathi, C 2025 置信度 0.66 Privacy Risks and ChallengesUser behavior and awarenessGDPR and Data ProtectionLegal and Regulatory FrameworksSocial Media Platforms
• FedMAC: Tackling Partial-Modality Missing in Federated Learning with Cross-Modal Aggregation and Contrastive Regularization
  Federated Learning (FL) is a method for training machine learning models using distributed data sources. It ensures privacy by allowing clients to collaboratively learn a shared global model while storing their data locally. However, a significant challenge ar…
  datacite Nguyen, Manh Duong, Nguyen, Trung Thanh, Pham, Huy Hieu, Hoang, Trong Nghia 等 2024 置信度 0.66 Machine Learning (cs.LG)Multimedia (cs.MM)FOS: Computer and information sciencesFOS: Computer and information sciences
• Bayesian Coreset Optimization for Personalized Federated Learning
  In a distributed machine learning setting like Federated Learning where there are multiple clients involved which update their individual weights to a single central server, often training on the entire individual client's dataset for each client becomes cumbe…
  datacite Chanda, Prateek, Modi, Shrey, Ramakrishnan, Ganesh 2025 置信度 0.66 Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciencesI.2.6; H.3.3
• FedCLF -- Towards Efficient Participant Selection for Federated Learning in Heterogeneous IoV Networks
  Federated Learning (FL) is a distributed machine learning technique that preserves data privacy by sharing only the trained parameters instead of the client data. This makes FL ideal for highly dynamic, heterogeneous, and time-critical applications, in particu…
  datacite Wijethilake, Kasun Eranda, Mahmood, Adnan, Sheng, Quan Z. 2025 置信度 0.66 Machine Learning (cs.LG)Artificial Intelligence (cs.AI)FOS: Computer and information sciencesFOS: Computer and information sciences
• A Privacy-Preserving Ecosystem for Developing Machine Learning Algorithms Using Patient Data: Insights from the TUM.ai Makeathon
  The integration of clinical data offers significant potential for the development of personalized medicine. However, its use is severely restricted by the General Data Protection Regulation (GDPR), especially for small cohorts with rare diseases. High-quality,…
  datacite Süwer, Simon, Mai, Mai Khanh, Klein, Christoph, Götzenberger, Nicola 等 2025 置信度 0.66 Distributed, Parallel, and Cluster Computing (cs.DC)FOS: Computer and information sciencesFOS: Computer and information sciences
• CollectiveOS & The Sovereign Mobile Super-Node
  CollectiveOS & The Sovereign Mobile Super-Node An Open-Science Architecture for Portable, Patent-Free AI Infrastructure Version 1.0 — October 2025 Author & Custodian:Mark Anthony Brewer — Human Global Science Collective (HGSC) Affiliation:Human Global Science …
  datacite Brewer, Mark Anthony 2025 置信度 0.66
• CollectiveOS & The Sovereign Mobile Super-Node
  CollectiveOS & The Sovereign Mobile Super-Node An Open-Science Architecture for Portable, Patent-Free AI Infrastructure Version 1.0 — October 2025 Author & Custodian:Mark Anthony Brewer — Human Global Science Collective (HGSC) Affiliation:Human Global Science …
  datacite Brewer, Mark Anthony 2025 置信度 0.66
• C1 -Internship L'Oréal 2024: Differential privacy for federated learning in Cosmetical Science. Application to safety data.
  datacite Bastien, Philippe 2023 置信度 0.66
• FedHybrid: Breaking the Memory Wall of Federated Learning via Hybrid Tensor Management
  Federated Learning (FL) emerges as a new learning paradigm that enables multiple devices to collaboratively train a shared model while preserving data privacy. However, one fundamental and prevailing challenge that hinders the deployment of FL on mobile device…
  datacite Tam, Kahou, Tian, Chunlin, Li, Li, Zhao, Haikai 等 2025 置信度 0.66 Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• Pursuing Overall Welfare in Federated Learning through Sequential Decision Making
  In traditional federated learning, a single global model cannot perform equally well for all clients. Therefore, the need to achieve the client-level fairness in federated system has been emphasized, which can be realized by modifying the static aggregation sc…
  datacite Hahn, Seok-Ju, Kim, Gi-Soo, Lee, Junghye 2024 置信度 0.66 Machine Learning (cs.LG)Distributed, Parallel, and Cluster Computing (cs.DC)Machine Learning (stat.ML)FOS: Computer and information sciencesFOS: Computer and information sciences
• Security and Privacy in AI Healthcare Systems: A Systematic Literature Review on Aligning Innovation with HIPAA Regulations and Hospital Cybersecurity
  Abstract Background: Artificial intelligence (AI) is revolutionizing healthcare delivery through innovations in diagnosis, personalized treatment, and operational efficiency. However, the growing integration of AI systems raises significant concerns about the …
  datacite Caetano, Rafael Magalhães 2025 置信度 0.66
• Security and Privacy in AI Healthcare Systems: A Systematic Literature Review on Aligning Innovation with HIPAA Regulations and Hospital Cybersecurity
  Abstract Background: Artificial intelligence (AI) is revolutionizing healthcare delivery through innovations in diagnosis, personalized treatment, and operational efficiency. However, the growing integration of AI systems raises significant concerns about the …
  datacite Caetano, Rafael Magalhães 2025 置信度 0.66
• Triple_MNIST_Segmentation_0134
  This is the official deposite of the Triple MNIST Segmentation 0134 dataset described and used in the article "Matthis Manthe et al., “Deep Domain Isolation and Sample Clustered Federated Learning for Semantic Segmentation,” in Machine Learning and Knowledge D…
  datacite Manthe, Matthis 2025 置信度 0.66
• Triple_MNIST_Segmentation_0134
  This is the official deposite of the Triple MNIST Segmentation 0134 dataset described and used in the article "Matthis Manthe et al., “Deep Domain Isolation and Sample Clustered Federated Learning for Semantic Segmentation,” in Machine Learning and Knowledge D…
  datacite Manthe, Matthis 2025 置信度 0.66
• Workforce Scheduling in Logistics Hubs Dataset
  This Workforce Scheduling and Operational Efficiency Dataset captures real-time, high-frequency operational data collected from a logistics hub over a 7-year period spanning from January 1, 2018, to December 31, 2024. The dataset includes hourly records for a …
  datacite San Bernardino Inland Empire Logistics Hub 2025 置信度 0.66
• Workforce Scheduling in Logistics Hubs Dataset
  This Workforce Scheduling and Operational Efficiency Dataset captures real-time, high-frequency operational data collected from a logistics hub over a 7-year period spanning from January 1, 2018, to December 31, 2024. The dataset includes hourly records for a …
  datacite San Bernardino Inland Empire Logistics Hub 2025 置信度 0.66
• Tackling Selfish Clients in Federated Learning
  Federated Learning (FL) is a distributed machine learning paradigm facilitating participants to collaboratively train a model without revealing their local data. However, when FL is deployed into the wild, some intelligent clients can deliberately deviate from…
  datacite Augello, Andrea, Gupta, Ashish, Re, Giuseppe Lo, Das, Sajal K. 2024 置信度 0.66 Machine Learning (cs.LG)Artificial Intelligence (cs.AI)Distributed, Parallel, and Cluster Computing (cs.DC)FOS: Computer and information sciencesFOS: Computer and information sciences
• FedGCS: A Generative Framework for Efficient Client Selection in Federated Learning via Gradient-based Optimization
  Federated Learning faces significant challenges in statistical and system heterogeneity, along with high energy consumption, necessitating efficient client selection strategies. Traditional approaches, including heuristic and learning-based methods, fall short…
  datacite Ning, Zhiyuan, Tian, Chunlin, Xiao, Meng, Fan, Wei 等 2024 置信度 0.66 Machine Learning (cs.LG)Distributed, Parallel, and Cluster Computing (cs.DC)FOS: Computer and information sciencesFOS: Computer and information sciences
• CONFIDENTIAL6G – Latest Updates from the Project M19 – M30
  This newsletter presents the latest updates from the Horizon Europe project CONFIDENTIAL6G (GA No. 101096435), covering activities from months 19 to 30. The issue highlights recent project achievements, events, and publications that advance confidentiality, pr…
  datacite Vasic, Jelena 2025 置信度 0.66 CONFIDENTIAL6GNewsletterData privacyAI/MLConfidential computing
• CONFIDENTIAL6G – Latest Updates from the Project M19 – M30
  This newsletter presents the latest updates from the Horizon Europe project CONFIDENTIAL6G (GA No. 101096435), covering activities from months 19 to 30. The issue highlights recent project achievements, events, and publications that advance confidentiality, pr…
  datacite Vasic, Jelena 2025 置信度 0.66 CONFIDENTIAL6GNewsletterData privacyAI/MLConfidential computing
• OptimES: Optimizing Federated Learning Using Remote Embeddings for Graph Neural Networks
  Graph Neural Networks (GNNs) have experienced rapid advancements in recent years due to their ability to learn meaningful representations from graph data structures. However, in most real-world settings, such as financial transaction networks and healthcare ne…
  datacite Naman, Pranjal, Simmhan, Yogesh 2025 置信度 0.66 Distributed, Parallel, and Cluster Computing (cs.DC)FOS: Computer and information sciencesFOS: Computer and information sciences
• Distribution-Controlled Client Selection to Improve Federated Learning Strategies
  Federated learning (FL) is a distributed learning paradigm that allows multiple clients to jointly train a shared model while maintaining data privacy. Despite its great potential for domains with strict data privacy requirements, the presence of data imbalanc…
  datacite Düsing, Christoph, Cimiano, Philipp 2025 置信度 0.66 Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• D8.4 - Seminars, workshop and events - Event Nr 1
  Deliverable D8.4, titled "Seminars, Workshops, and Events," is a key component of the dAIEDGE project, funded by the European Union's Horizon Europe research and innovation program. This deliverable results from Task T8.3, which focuses on the dissemination an…
  datacite AIR Institute 2025 置信度 0.66
• D8.4 - Seminars, workshop and events - Event Nr 1
  Deliverable D8.4, titled "Seminars, Workshops, and Events," is a key component of the dAIEDGE project, funded by the European Union's Horizon Europe research and innovation program. This deliverable results from Task T8.3, which focuses on the dissemination an…
  datacite AIR Institute 2025 置信度 0.66
• An Empirical Analysis of Secure Federated Learning for Autonomous Vehicle Applications
  Federated Learning lends itself as a promising paradigm in enabling distributed learning for autonomous vehicles applications and ensuring data privacy while enhancing and refining predictive model performance through collaborative training on edge client vehi…
  datacite Mia, Md Jueal, Amini, M. Hadi 2025 置信度 0.66 Distributed, Parallel, and Cluster Computing (cs.DC)FOS: Computer and information sciences
• FairEquityFL -- A Fair and Equitable Client Selection in Federated Learning for Heterogeneous IoV Networks
  Federated Learning (FL) has been extensively employed for a number of applications in machine learning, i.e., primarily owing to its privacy preserving nature and efficiency in mitigating the communication overhead. Internet of Vehicles (IoV) is one of the pro…
  datacite Islam, Fahmida, Mahmood, Adnan, Mukhtiar, Noorain, Wijethilake, Kasun Eranda 等 2025 置信度 0.66 Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciencesI.2.6; I.2.11; C.2.468T05 = Learning and adaptive systems (AI) 68T07 = Artificial neural networks and deep learning 68M14 = Distributed systems
• Metacampus as a Base for a Unite! Blended Intensive Programme (BIP): Competencies for Collaborative Teaching in Joint Programme (Report 04/2025)
  The report “Metacampus as a Base for a Unite! Blended Intensive Programme (BIP): Competencies for Collaborative Teaching in Joint Programmes” describes the Erasmus + Blended Intensive Program (BIP) held from June to October 2024 under the Unite! University Net…
  datacite Acosta-Garcia, Marcela, Galante, Lorenzo, Kauppinen, Tomi, Keller, Elizabeth 等 2025 置信度 0.66
• Gradient descent-type methods
  crossref Quoc Tran-Dinh, Marten van Dijk 2024-02-23T09:48:22Z 置信度 0.70
• Federated Learning in Edge Computing Environments: Opportunities, Challenges, and Future Directions
  crossref Shaveta x 2024-09-06T12:36:26Z 置信度 0.70
• Federated Learning-Based Frameworks for Trusted and Secure Communication in IoVs
  Federated learning is a machine learning approach that allows many parties to collaborate on training a model without disclosing their raw data. Federated learning is critical in the context of the Internet of Vehicles (IoVs) because it allows cars to exchange…
  crossref Kapil Kumar Sharma, Gopal Krishna, Gaurav Singh Negi, Jitendra Kumar Gupta 2024-12-26T08:01:16Z 置信度 0.70
• Adapting federated learning-based AI models to dynamic cyberthreats in pervasive IoT environments
  crossref S. Tamizharasi, P. Rubini, S. Saravana Kumar, Daniel Arockiam 2025-01-20T05:27:31Z 置信度 0.70
• Unleash Federated Machine Learning and Internet of Medical Things (IoMT) for Disease Screening and Enhancement of Smart Healthcare
  crossref Bhupinder Singh, Christian Kaunert 2024-09-20T17:25:42Z 置信度 0.70
• Federated Learning in Customer-Centric Applications: Balancing Privacy, Personalization and Performance
  crossref Xinxiang Gao 2026-02-19T17:40:06Z 置信度 0.70
• Federated Learning-Based EfficientNet in Brain Tumor Classification
  crossref Baicheng Chen 2024-09-19T12:37:25Z 置信度 0.70
• Consistent Post-Hoc Explainability in Federated Learning through Federated Fuzzy Clustering
  crossref Pietro Ducange, Francesco Marcelloni, Alessandro Renda, Fabrizio Ruffini 2024-08-05T17:28:08Z 置信度 0.70
• Safeguarding Data Privacy and Security in Federated Learning Systems
  crossref Wasswa Shafik, Kassim Kalinaki, Khairul Eahsun Fahim, Mumin Adam 2024-07-29T08:04:57Z 置信度 0.70
• Image Classification Based on Federated Learning and PFLlib
  crossref Weiqing Fu 2024-09-19T12:37:25Z 置信度 0.70
• Federated Learning Approach to Safeguard User Privacy
  Current intrusion detection models based on machine learning require reliable datasets, but the public dataset updates are typically delayed after new attacks, which slows down the model's update speed. Also, to train the existing model, the data needs to be s…
  crossref Aryan Bansal, A. Karmel 2024-05-02T09:01:10Z 置信度 0.70
• A New Privacy Protection Method for Federated Learning in Smart Grids
  crossref Jianguo Wei 2024-12-07T06:01:53Z 置信度 0.70
• Data valuation in federated learning
  crossref Zhaoxuan Wu, Xinyi Xu, Rachael Hwee Ling Sim, Yao Shu 等 2024-02-23T09:49:27Z 置信度 0.70
• Quantum Federated Learning for Image Classification
  crossref Leo Sünkel, Philipp Altmann, Michael Kölle, Thomas Gabor 2024-02-29T00:30:53Z 置信度 0.70
• Exploring the Impact of Data Heterogeneity in Federated Learning for Fraud Detection
  crossref Zhiqiu Wang 2025-07-01T23:49:23Z 置信度 0.70
• Front Matter
  crossref 2025-01-20T05:27:31Z 置信度 0.70
• A Self-Organized Moe Framework for Distributed Federated Learning
  crossref Jungjae Lee, Wooseong Kim 2024-11-12T15:10:15Z 置信度 0.70
• Fedsr: Federated Learning for Image Super-Resolution Via Detail-Assisted Contrastive Learning
  crossref Yue Yang, Xiaodong Ren, Liangjun Ke 2024-04-29T20:21:19Z 置信度 0.70
• The Advancements and Future Prospects of Federated Learning-Based Methods for Biometrics
  crossref Yifan Zhang 2025-07-01T23:50:26Z 置信度 0.70
• Federated Deep Learning Systems in Healthcare
  crossref Ashraful Reza Tanjil, Fahim Mohammad Adud Bhuiyan, Mohammad Abu Tareq Rony, Kamanashis Biswas 2024-07-29T08:04:57Z 置信度 0.70
• Federated, Split, or Split-Federated Learning for Network Intelligence in 6G? a Comprehensive Investigation of Deployment Challenges
  crossref Suman Paul 2025-08-29T17:39:27Z 置信度 0.70
• Federated Learning
  crossref Jayakrushna Sahoo, Mariya Ouaissa, Akarsh K. Nair 2024-05-30T10:30:21Z 置信度 0.70
• Knowledge and Model-Driven Deep Reinforcement Learning for Federated Edge Learning
  crossref Yangchen Li, Lingzhi Zhao, Feng Yang 2025-03-11T17:30:35Z 置信度 0.70
• Privacy-Preserving Federated Learning Compatible with Robust Aggregators
  crossref Zeinab Alebouyeh, Amir Jalaly Bidgoly 2024-07-01T22:18:59Z 置信度 0.70
• Current Trends in Federated Learning: A Review
  — In recent years Machine Learning (ML) and Deep Learning (DL) are advancing at a rapid pace. With new groundbreaking and exciting research wonders such as Chat GPT a Generative Transformer model, DALL-E, Stable Diffusion, etc… more and more everyday user is u…
  crossref Aayush Sharma, Harjeet Kaur, Deepak Prashar 2024-04-05T10:16:57Z 置信度 0.70
• A Clustered Federated Learning Approach for Heart Failure Prediction
  crossref Debmalya Sur, Sachin Tripathi 2024-07-25T02:15:15Z 置信度 0.70
• Overview of Federated Learning and Its Advantages
  Federated learning (FL) is closely linked to decentralized education. A decentralized system primarily targets expediting the operation phase, whereas federated learning concentrates on constructing a cooperative prototype devoid of privacy disclosure. Some of…
  crossref Alisha Kakkar, Sudesh Kumar 2024-05-02T09:01:10Z 置信度 0.70
• Blockchain-Enhanced Federated Learning for Privacy-Preserving Collaboration
  crossref Pawan Whig, Balaram Yadav Kasula, Nikhitha Yathiraju, Anupriya Jain 等 2024-11-29T12:15:57Z 置信度 0.70
• Privacy-Preserving Federated Learning Compatible with Robust Aggregators
  crossref Zeinab Alebouyeh, Amir Jalaly Bidgoly 2024-04-13T18:18:29Z 置信度 0.70
• Evaluating Legal Compliance of Federated Learning Tools for the European Health Data Space (EHDS)
  crossref Silvio Gonçalves, Raimundo Lucas, Ramiro Viana, Thales Lopes 等 2025-01-21T18:22:35Z 置信度 0.70
• Trustworthiness, Privacy, and Security in Federated Learning
  crossref Sisi Zhou, Lijun Xiao, Yufeng Xiao, Meikang Qiu 2024-09-03T07:02:43Z 置信度 0.70
• Federated Learning in Smart Cities
  crossref Seyedeh Yasaman Hosseini Mirmahaleh, Amir Masoud Rahmani 2024-11-22T21:19:26Z 置信度 0.70
• Proof-of-Collaborative-Learning: A Multi-winner Federated Learning Consensus Algorithm
  crossref Amirreza Sokhankhosh, Sara Rouhani 2024-09-18T17:51:07Z 置信度 0.70
• Federated and Transfer Learning Applications
  crossref 2024-04-09T10:11:49Z 置信度 0.70
• Collaborative intelligence: federated learning-enabled edge computing in Industry 5.0
  crossref M. Ramalingam, Gokul Yenduri, Praveen Kumar Reddy Maddikunta, Praveen Kumar Donta 等 2025-01-13T08:38:15Z 置信度 0.70
• Federated data access and federated learning: improved data sharing, AI model development, and learning in intensive care
  crossref Michel E. van Genderen, Maurizio Cecconi, Christian Jung 2024-04-18T06:18:58Z 置信度 0.70
• A Federated Learning-POMDP Approach for Network Intrusion Detection
  crossref Curtis Rookard, Anahita Khojandi 2025-03-28T02:39:19Z 置信度 0.70
• Fake Currency Identification Using Artificial Intelligence and Federated Learning
  crossref Syed Zahiruddin, Vamsi Krishna Kadiri, Valli Bhasha Achukatla, Pavan Kumar Kattela 等 2024-11-29T17:15:57Z 置信度 0.70
• Front Matter
  crossref 2025-01-13T08:38:15Z 置信度 0.70
• About Vector Quantization and its Privacy in Federated Learning
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