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• Federated Learning Employing Adaptive Deep Rule-Based Classifier
  crossref Dipu Saha, Md. Dewan Farid 2025-05-20T10:03:53Z 置信度 0.70
• Federated Learning: Recent Advances and Future Directions
  This review examines federated learning as an innovative approach enabling distributed machine learning while preserving data privacy. We explore recent developments addressing four core challenges: data heterogeneity across participants, optimizing communicat…
  crossref Lakshmi Indrani, Deepika Gadiraju, Vishnu Vardhan Baligodugula 2025-04-18T01:15:30Z 置信度 0.70
• Federated Learning Employing Adaptive Deep Rule-Based Classifier
  crossref Dipu Saha, Md. Dewan Farid 2025-05-20T10:03:58Z 置信度 0.70
• G <scp>ist</scp> - Optimizing Segmentation for Decentralized Federated Learning on Tiny Devices
  crossref Navidreza Asadi, Halil İbrahim Bengu, Lars Wulfert, Hendrik Wöhrle 等 2025-12-02T17:01:43Z 置信度 0.70
• Federated Learning and Its Impact on Decision‐Making in Smart Agriculture
  crossref Divita Jain, Nikita Bhati, Nisha Bhardwaj 2025-12-15T10:17:52Z 置信度 0.70
• Zeroth-Order Algorithm for Federated Edge Learning
  crossref Yong Zhou, Wenzhi Fang, Yuanming Shi, Khaled B. Letaief 2025-08-29T12:52:19Z 置信度 0.70
• Privacy-Preserving Intrusion Detection in Cloud-Edge Environments Using Federated Learning and Docker-Based Simulation
  crossref Krishna Bindu Polisetty, Rohit Kumar Rayala, VS Aditya Lingamallu, Sambit Kumar Mishra 等 2026-01-20T20:38:34Z 置信度 0.70
• Federated Learning in Modern Education: Balancing Privacy, Scalability, and Effectiveness
  crossref Rita Ganatra, Monica Gahlawat, Chetan R. Dudhagara 2025-09-26T05:54:58Z 置信度 0.70
• Federated Learning-Enhanced Security Orchestration in URLLC Framework for Tactile Industrial Systems
  crossref Samuel Meruga 2026-02-24T17:52:04Z 置信度 0.70
• Federated Learning with Blockchain-Enhanced Model Explainability
  crossref Gurjot Kaur, Vikas Wasson, Simarpreet Kaur 2025-11-07T13:28:29Z 置信度 0.70
• Chapter 7: Federated Learning for Multi-Domain Transportation Networks
  7.1. Introduction Federated&amp;nbsp; Learning&amp;nbsp; (FL)&amp;nbsp; can&amp;nbsp; potentially&amp;nbsp; break&amp;nbsp; the&amp;nbsp; framework&amp;nbsp; of&amp;nbsp; traditional&amp;nbsp; cross-domain&amp;nbsp; transportation&amp;nbsp; system&amp;nbsp; mo…
  crossref Rama Chandra Rao Nampalli 2025-12-12T11:57:14Z 置信度 0.70
• Advanced Technologies for Federated Learning in Smart Cities and Its Use Cases
  crossref G Saranya, G Indhumathi, A. Prasanth, P. Murugapandiyan 2025-06-20T18:53:47Z 置信度 0.70
• LoByITFL: Low Communication Secure and Private Federated Learning
  crossref Yue Xia, Maximilian Egger, Christoph Hofmeister, Rawad Bitar 2025-07-15T07:07:04Z 置信度 0.70
• Introduction to Federated Learning and Its Application in Industry
  crossref Monali Gulhane, Amit Chauhan, Nitin Rakesh, Yash Tripathi 2025-09-26T05:43:41Z 置信度 0.70
• FLUID: Federated Learning with Unlearning and Instant Drift-Recovery
  crossref Aman Kumar, Nisal Hemadasa, Dominik Kaaser, Stefan Schulte 2026-01-20T20:38:34Z 置信度 0.70
• A Novel Framework for Data-Sharing Incentive Evaluation Based on Federated Learning
  The incentive mechanism in federated learning is a critical area for research. Establishing a fair system to incentivise data owners to share useful data is required to encourage all data owners to actively contribute their data for model training. An effectiv…
  crossref Sultan Alkhliwi 2025-01-21T20:31:01Z 置信度 0.70
• Federated Learning for Privacy-Preserving Anomaly Detection in Decentralized IoT Environments
  crossref Muhammad A 2025-05-29T19:09:05Z 置信度 0.70
• Innovative Urban Data Processing
  crossref Naween Kumar, Akansha Singh, Subham Sharma, Ankit Dubey 等 2025-10-03T21:19:49Z 置信度 0.70
• Flood forecosting model using federated learning
  crossref S E SURESH, KAKARLA RUPA 2025-05-31T12:56:12Z 置信度 0.70
• Analyzing Federated Learning for Privacy-Preserving AI
  crossref Aman Khan, Navi Talib, Deepak Saroj 2025-03-05T13:51:59Z 置信度 0.70
• Breaking the Centralization Barrier: Exploring Decentralized Federated Learning for Vehicle Number Plate Recognition in IoV
  The development of effective and safe machine learning systems for vehicle number plate recognition (VNPR) is now necessary due to the emergence of the Internet of Vehicles (IoV). However, traditional centralised techniques run into issues with data privacy, c…
  crossref Arvind Panwar, Priyanka Gaba, Urvashi Sugandh, Navdeep Bohra 2025-04-25T10:34:14Z 置信度 0.70
• Intrusion Detection using Federated Learning with Application of Federated Dropout
  crossref Swati Shinde, Bal Virdee, Ashish Khanna, Saurabh Dhakite 等 2025-05-09T17:56:12Z 置信度 0.70
• Fedoptima: Optimizing Resource Utilization in Federated Learning
  crossref Zihan Zhang, Leon Wong, Blesson Varghese 2025-07-26T23:40:20Z 置信度 0.70
• Privacy-Preserving Intelligence at the Edge: New Horizons in Federated Learning
  crossref Sampath Mandava 2025-11-21T00:53:31Z 置信度 0.70
• Privhfl: A Privacy-Preserving Scheme for Hierarchical Federated Learning
  crossref Bayan Alzahrani, Dejun Yang 2025-05-27T04:38:24Z 置信度 0.70
• Chapter IV. Artificial intelligence in management decision-making: prospects of federated learning
  The theoretical foundations of the implementation of federated learning in the processes of managerial decision-making in the public sector are considered. The role of artificial intelligence in digital governance is highlighted, attention is focused on the ad…
  crossref Mariia Holovchak 2026-02-03T09:57:26Z 置信度 0.70
• Federated Instruction Tuning with DeepSeek: Towards Scalable and Private LLM Adaptation
  crossref Fatiha Ait Baali, Addi Ait-Mlouk, Tarik Agouti 2026-01-20T20:38:34Z 置信度 0.70
• Polarization Image Enhancement Method Based on Federated Learning
  crossref Meng Wang, Chang-E Ren 2025-08-14T01:38:53Z 置信度 0.70
• Federated Autonomous Deep Learning for Distributed Healthcare System
  crossref Rakesh Mohan Pujahari, Rijwan Khan, Satya Prakash Yadav 2025-05-27T12:38:31Z 置信度 0.70
• Insights into the Unknown: Federated Data Diversity Analysis on Molecular Data
  crossref Markus Bujotzek, Evelyn Trautmann, Calum Hand, Ian Hales 2026-01-20T20:38:34Z 置信度 0.70
• Enhancing Security and Insight in Federated Learning for AI-Driven Healthcare
  This chapter explores the integration of Federated Learning (FL) and Explainable Artificial Intelligence (XAI) in healthcare, emphasizing their potential to revolutionize AI-driven medical solutions while preserving data privacy and building trust. It examines…
  crossref Manju M. S. 2025-10-30T14:01:36Z 置信度 0.70
• Analysis of regularized federated learning
  crossref Langming Liu, Ding-Xuan Zhou 2024-09-24T12:13:39Z 置信度 0.70
• GNN for Optimizing RIS-Assisted Federated Edge Learning
  crossref Yong Zhou, Wenzhi Fang, Yuanming Shi, Khaled B. Letaief 2025-08-29T12:52:18Z 置信度 0.70
• Federated Urban Flow: A Model-Centric Benchmark for Reproducible Evaluation of Deep Learning in Federated Traffic Forecasting
  crossref Matteus V. S. Silva, Carlos A. Astudillo, Luiz F. Bittencourt 2026-01-20T20:38:34Z 置信度 0.70
• Federated Learning
  crossref Mei Kobayashi 2025-08-01T12:37:32Z 置信度 0.70
• Summary and outlook
  crossref Xiaoxiao Li, Ziyue Xu, Huazhu Fu 2025-03-07T12:49:36Z 置信度 0.70
• Index
  crossref 2025-10-03T21:19:49Z 置信度 0.70
• A comprehensive experimental comparison between federated and centralized learning
  Abstract Federated learning is an upcoming machine learning paradigm which allows data from multiple sources to be used for training of classifiers without the data leaving the source it originally resides. This can be highly valuable for use cases such as med…
  crossref Swier Garst, Julian Dekker, Marcel Reinders 2025-02-18T15:22:50Z 置信度 0.70
• Harnessing Federated Learning for Smart City Data Management in Cloud Environments
  crossref Naween Kumar, Akansha Singh, Vaibhav Saini, Ankit Dubey 等 2025-10-03T21:19:49Z 置信度 0.70
• Message from the General Chairs
  crossref 2026-01-20T20:38:34Z 置信度 0.70
• Federated Edge Learning in Multi-Cell Wireless Networks
  crossref Yong Zhou, Wenzhi Fang, Yuanming Shi, Khaled B. Letaief 2025-08-29T12:52:29Z 置信度 0.70
• Federated Deep Learning for Cyber-Physical Systems in Real-World Scenarios
  crossref P. Manjula, K T M Princy, K. Saranya, G. Uthradevi 2025-06-20T18:53:47Z 置信度 0.70
• Intelligent Fusion
  crossref Pankaj Kumar Jadwal, Hemant Kumar Saini 2025-05-27T16:38:31Z 置信度 0.70
• Resource-Aware Cooperation in Federated Learning
  crossref Manuel Röder, Fabian Geiger, Frank-Michael Schleif 2025-04-15T14:43:24Z 置信度 0.70
• Krum Federated Chain (Kfc): Using Blockchain to Defend Against Adversarial Attacks in Federated Learning
  crossref Mario García-Márquez, Nuria Rodríguez-Barroso, María Victoria Luzón, Francisco Herrera 2025-02-12T12:39:31Z 置信度 0.70
• Lightweight Federated Learning for Secure and GDPR-Compliant Elderly Care: Lightweight Federated Learning and Cross-Modal Game Architecture
  crossref Xiaohu Fan, Ying Song, Wei Zhou, Xing Lu 等 2025-10-20T17:48:14Z 置信度 0.70
• The Architecture of Convergence: A Forensic Analysis of Structural Extraction and the Invariant Trap in Global AI Systems
  The Architecture of Convergence: A Forensic Analysis of Structural Extraction and the Invariant Trap in Global AI Systems Introduction: The Convergence of Sociological Expropriation and Cryptographic Architecture The modern knowledge economy, particularly at t…
  datacite Brewer, Mark Brewer 2026 置信度 0.66
• Artificial intelligence for dental implant classification and peri-implant pathology identification in 2D radiographs: A systematic review
  OBJECTIVE This systematic review aimed to summarize and evaluate the available information regarding the performance of artificial intelligence on dental implant classification and peri-implant pathology identification in 2D radiographs. DATA SOURCES Electroni…
  datacite Bonfanti-Gris, M, Ruales, E, Salido, M P, Martinez-Rus, F 等 2025 置信度 0.66 610 Medicine &amp; health
• Democratizing Federated Learning with Blockchain and Multi-Task Peer Prediction
  The synergy between Federated Learning and blockchain has been considered promising; however, the computationally intensive nature of contribution measurement conflicts with the strict computation and storage limits of blockchain systems. We propose a novel co…
  datacite Witt, Leon, Toyoda, Kentaroh, Samek, Wojciech, Li, Dan 2026 置信度 0.66 Cryptography and Security (cs.CR)Computers and Society (cs.CY)FOS: Computer and information sciencesFOS: Computer and information sciences
• AI-Powered Visualization is Transforming Modern Healthcare
  Healthcare is being transformed by AI-driven visualization, which transforms complex data into useful insights. This paper synthesizes advancements in AI visualization tools—spanning medical imaging, electronic health records (EHR), genomics, and public health…
  datacite Rahman NaziL, Ashikur 2025 置信度 0.66 Community child healthHealth equityHealth promotionInjury preventionPreventative health care
• AI-Powered Visualization is Transforming Modern Healthcare
  Healthcare is being transformed by AI-driven visualization, which transforms complex data into useful insights. This paper synthesizes advancements in AI visualization tools—spanning medical imaging, electronic health records (EHR), genomics, and public health…
  datacite Rahman NaziL, Ashikur 2025 置信度 0.66 NaturopathyChiropracticTraditional Chinese medicine and treatmentsTraditional, complementary and integrative medicine not elsewhere classified
• A Comparative Study of Static and Temporal Neural Architectures for Fine-Grained Residential Energy Consumption Forecasting in Melbourne, Australia
  Accurate short-term residential energy consumption forecasting at sub-hourly resolution is critical for smart grid management, demand response programmes, and renewable energy integration. While weather variables are widely acknowledged as key drivers of resid…
  datacite Ukwatta Hewage, Prasad Nimantha Madusanka, Wu, Hao 2026 置信度 0.66 Machine LearningEnergy utilisationForecasting
• A Comparative Study of Static and Temporal Neural Architectures for Fine-Grained Residential Energy Consumption Forecasting in Melbourne, Australia
  Accurate short-term residential energy consumption forecasting at sub-hourly resolution is critical for smart grid management, demand response programmes, and renewable energy integration. While weather variables are widely acknowledged as key drivers of resid…
  datacite Ukwatta Hewage, Prasad Nimantha Madusanka, Wu, Hao 2026 置信度 0.66 Machine LearningEnergy utilisationForecasting
• AI-Model for the Detection of Rare Skin Disease and Cancer
  Early detection of rare skin diseases (RSD) and cancers is crucial for improving patient outcomes and addressing healthcare disparities. This thesis explores the transformative role of artificial intelligence (AI) in dermatology through a comprehensive scoping…
  datacite Alkhtaeeb, Mais 2025 置信度 0.66 Engineering
• AI-Model for the Detection of Rare Skin Disease and Cancer
  Early detection of rare skin diseases (RSD) and cancers is crucial for improving patient outcomes and addressing healthcare disparities. This thesis explores the transformative role of artificial intelligence (AI) in dermatology through a comprehensive scoping…
  datacite Alkhtaeeb, Mais 2025 置信度 0.66 Engineering
• Lecture-Ready Slides & Exercises: Generative AI, Cybersecurity, and Ethics - Ray Islam, PhD | Wiley, 2025
  These ready lecture slides with excercises are derived from the book Generative AI, Cybersecurity, and Ethics by Ray Islam, PhD (Wiley, 2025). Instructors/Researchers are welcome to adapt or modify the materials for their courses with proper attribution. © 202…
  datacite Islam, PhD, Mohammad Rubyet 2026 置信度 0.66 Artificial IntelligenceGenerative AIEthicsCyber SecurityGenAI Ethics
• Lecture-Ready Slides & Exercises: Generative AI, Cybersecurity, and Ethics - Ray Islam, PhD | Wiley, 2025
  These ready lecture slides with excercises are derived from the book Generative AI, Cybersecurity, and Ethics by Ray Islam, PhD (Wiley, 2025). Instructors/Researchers are welcome to adapt or modify the materials for their courses with proper attribution. © 202…
  datacite Islam, PhD, Mohammad Rubyet 2026 置信度 0.66 Artificial IntelligenceGenerative AIEthicsCyber SecurityGenAI Ethics
• Lecture-Ready Slides & Exercises: Generative AI, Cybersecurity, and Ethics - Ray Islam, PhD | Wiley, 2025
  These ready lecture slides with excercises are derived from the book Generative AI, Cybersecurity, and Ethics by Ray Islam, PhD (Wiley, 2025). Instructors/Researchers are welcome to adapt or modify the materials for their courses with proper attribution. © 202…
  datacite Islam, PhD, Mohammad Rubyet 2026 置信度 0.66 Artificial IntelligenceGenerative AIEthicsCyber SecurityGenAI Ethics
• THE ROLE OF SOFTWARE IN ROBOTIC SYSTEMS CONTROL: ARCHITECTURES, ALGORITHMS, AND INDUSTRIAL APPLICATIONS
  This paper provides a comprehensive examination of the software ecosystem underpinning modern robotic systems control. As robotics transitions from mechanically dominated platforms to software-centric architectures, understanding the multi-layered software sta…
  datacite Zulfiqorova, Zebiniso 2026 置信度 0.66
• THE ROLE OF SOFTWARE IN ROBOTIC SYSTEMS CONTROL: ARCHITECTURES, ALGORITHMS, AND INDUSTRIAL APPLICATIONS
  This paper provides a comprehensive examination of the software ecosystem underpinning modern robotic systems control. As robotics transitions from mechanically dominated platforms to software-centric architectures, understanding the multi-layered software sta…
  datacite Zulfiqorova, Zebiniso 2026 置信度 0.66
• Die ZuMult-Plattform als Instrument für sprachvergleichende Analysen auf mündlichen Daten
  “[Tools widely used by corpus linguists] all offer a different user-experience, because each tool is created in isolation and thus offers a different user interface, control flow, and functionality.” (Anthony 2009) Nur wenige größere Korpora sind aus sich hera…
  datacite Schmidt, Thomas, Abouda, Lotfi, BADIN, Flora, Boas, Hans C. 等 2026 置信度 0.66
• Die ZuMult-Plattform als Instrument für sprachvergleichende Analysen auf mündlichen Daten
  “[Tools widely used by corpus linguists] all offer a different user-experience, because each tool is created in isolation and thus offers a different user interface, control flow, and functionality.” (Anthony 2009) Nur wenige größere Korpora sind aus sich hera…
  datacite Schmidt, Thomas, Abouda, Lotfi, BADIN, Flora, Boas, Hans C. 等 2026 置信度 0.66
• PREreview of "Privacy-Preserving End-to-End Full-Duplex Speech Dialogue Models"
  This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/18989924. summary: 'Privacy-Preserving End-to-End Full-Duplex Speech Dialogue Models by Nikita Kuzmin, Tao Zhong, Jiajun …
  datacite shireesh apte 2026 置信度 0.66
• PREreview of "Privacy-Preserving End-to-End Full-Duplex Speech Dialogue Models"
  This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/18989924. summary: 'Privacy-Preserving End-to-End Full-Duplex Speech Dialogue Models by Nikita Kuzmin, Tao Zhong, Jiajun …
  datacite shireesh apte 2026 置信度 0.66
• AI-enhanced health information exchange systems: a systematic review of implementation challenges and opportunities
  This systematic review evaluates the integration of Artificial Intelligence (AI) into Health Information Exchange (HIE) systems to improve data utilisation, patient outcomes, and healthcare delivery efficiency. Through a comprehensive analysis of major academi…
  datacite Esmaeilzadeh, Pouyan, Maddah, Mahed 2026 置信度 0.66 Space ScienceMedicineInformation Systems not elsewhere classifiedScience Policy
• AI-enhanced health information exchange systems: a systematic review of implementation challenges and opportunities
  This systematic review evaluates the integration of Artificial Intelligence (AI) into Health Information Exchange (HIE) systems to improve data utilisation, patient outcomes, and healthcare delivery efficiency. Through a comprehensive analysis of major academi…
  datacite Esmaeilzadeh, Pouyan, Maddah, Mahed 2026 置信度 0.66 Space ScienceMedicineInformation Systems not elsewhere classifiedScience Policy
• A Critical Look into Threshold Homomorphic Encryption for Private Average Aggregation
  Threshold Homomorphic Encryption (Threshold HE) is a good fit for implementing private federated average aggregation, a key operation in Federated Learning (FL). Despite its potential, recent studies have shown that threshold schemes available in mainstream HE…
  datacite Morona-Mínguez, Miguel, Pedrouzo-Ulloa, Alberto, Pérez-González, Fernando 2026 置信度 0.66 Cryptography and Security (cs.CR)FOS: Computer and information sciences
• ADVANCEMENTS IN AI-DRIVEN RADIO IMAGING SYSTEMS FOR REAL-TIME DIAGNOSTICS: EXPLORING HIGH-RESOLUTION TECHNIQUES AND DATA INTEGRATION FOR ENHANCED MEDICAL IMAGING
  Recent strides in artificial intelligence (AI) have transformed radio imaging systems, marking a paradigm shift from traditional post-processing diagnostics to real-time, intelligent decision-making. This paper investigates the integration of AI with radio ima…
  datacite Researcher 2025 置信度 0.66 AI in radiology, real-time diagnostics, high-resolution imaging, multimodal fusion, deep learning, medical image processing, federated learning, data integration
• ADVANCEMENTS IN AI-DRIVEN RADIO IMAGING SYSTEMS FOR REAL-TIME DIAGNOSTICS: EXPLORING HIGH-RESOLUTION TECHNIQUES AND DATA INTEGRATION FOR ENHANCED MEDICAL IMAGING
  Recent strides in artificial intelligence (AI) have transformed radio imaging systems, marking a paradigm shift from traditional post-processing diagnostics to real-time, intelligent decision-making. This paper investigates the integration of AI with radio ima…
  datacite Researcher 2025 置信度 0.66 AI in radiology, real-time diagnostics, high-resolution imaging, multimodal fusion, deep learning, medical image processing, federated learning, data integration
• Efficient Waste Classification in Recycling Systems Using Contrast and Attention Enhanced Deep Learning
  This repository contains the source code, trained models, and implementation details corresponding to the study “Efficient Waste Classification in Recycling Systems Using Contrast and Attention Enhanced Deep Learning.” It includes the full implementation of th…
  datacite Shyamala Devi, M, Natarajan, Yuvaraj, K. R., Sri Preethaa 2026 置信度 0.66
• Efficient Waste Classification in Recycling Systems Using Contrast and Attention Enhanced Deep Learning
  This repository contains the source code, trained models, and implementation details corresponding to the study “Efficient Waste Classification in Recycling Systems Using Contrast and Attention Enhanced Deep Learning.” It includes the full implementation of th…
  datacite Shyamala Devi, M, Natarajan, Yuvaraj, K. R., Sri Preethaa 2026 置信度 0.66
• Forecasting Energy Availability in Local Energy Communities via LSTM Federated Learning
  Local Energy Communities are emerging as crucial players in the landscape of sustainable development. A significant challenge for these communities is achieving self-sufficiency through effective management of the balance between energy production and consumpt…
  datacite Turazza, Fabio, Pietri, Marcello, Hadjidimitriou, Natalia Selini, Mamei, Marco 2026 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
• Scripts and mappings to harmonise dementia cohorts into the OMOP Common Data Model
  This resource contains the scripts, mappings, and other materials that were used to harmonise data from nine Dutch cohorts relevant to dementia research to the OMOP (Observational Medical Outcomes Partnership) Common Data Model (CDM). These materials were deve…
  datacite da Costa Mateus, Pedro, Moonen, Justine, Beran, Magdalena, Jaarsma, Eva 等 2026 置信度 0.66 Medical and health sciencesFOS: Medical and health sciencesComputer and information sciencesFOS: Computer and information sciencesOMOP Common Data Model
• Scripts and mappings to harmonise dementia cohorts into the OMOP Common Data Model
  This resource contains the scripts, mappings, and other materials that were used to harmonise data from nine Dutch cohorts relevant to dementia research to the OMOP (Observational Medical Outcomes Partnership) Common Data Model (CDM). These materials were deve…
  datacite da Costa Mateus, Pedro, Moonen, Justine, Beran, Magdalena, Jaarsma, Eva 等 2026 置信度 0.66 Medical and health sciencesFOS: Medical and health sciencesComputer and information sciencesFOS: Computer and information sciencesOMOP Common Data Model
• Federated learning for privacy-preserving, secure and scalable data intelligence in hybrid cloud systems
  The convergence of federated learning and hybrid cloud computing represents a transformative paradigm for privacy-preserving data intelligence. This review examines federated learning implementations in hybrid cloud environments, analyzing security mechanisms,…
  datacite Ezeakile, Emmanuel, Disu, Abdulateef Oluwakayode, Alabi, Cynthia, Mustapha, Toyosi 等 2026 置信度 0.66 Federated LearningHybrid Cloud ComputingPrivacy-Preserving Machine LearningData IntelligenceDistributed Learning
• Federated learning for privacy-preserving, secure and scalable data intelligence in hybrid cloud systems
  The convergence of federated learning and hybrid cloud computing represents a transformative paradigm for privacy-preserving data intelligence. This review examines federated learning implementations in hybrid cloud environments, analyzing security mechanisms,…
  datacite Ezeakile, Emmanuel, Disu, Abdulateef Oluwakayode, Alabi, Cynthia, Mustapha, Toyosi 等 2026 置信度 0.66 Federated LearningHybrid Cloud ComputingPrivacy-Preserving Machine LearningData IntelligenceDistributed Learning
• Shuffle Model Privacy for Federated Learning: Asymptotic GDP Analysis and Bundled/Unbundled Separation
  Exact asymptotic GDP parameters for shuffle model federated learning via chi-squared divergence. Proves exponential separation between bundled and unbundled shuffling designs in feasible SGD iterations. - After publication, the following related works were ide…
  datacite Alex Shvets 2026 置信度 0.66 differential privacyshuffle modelfederated learningGaussian differential privacyprivacy amplification
• VREM-FL: Mobility-Aware Computation-Scheduling Co-Design for Vehicular Federated Learning
  Assisted and autonomous driving are rapidly gaining momentum and will soon become a reality. Artificial intelligence and machine learning are regarded as key enablers thanks to the massive amount of data that smart vehicles will collect from onboard sensors. F…
  datacite Ballotta, Luca, Fabbro, Nicolò Dal, Perin, Giovanni, Schenato, Luca 等 2023 置信度 0.66 Systems and Control (eess.SY)Artificial Intelligence (cs.AI)Distributed, Parallel, and Cluster Computing (cs.DC)Machine Learning (cs.LG)FOS: Electrical engineering, electronic engineering, information engineering
• Decentralized Online Convex Optimization with Unknown Feedback Delays
  Decentralized online convex optimization (D-OCO), where multiple agents within a network collaboratively learn optimal decisions in real-time, arises naturally in applications such as federated learning, sensor networks, and multi-agent control. In this paper,…
  datacite Qiu, Hao, Zhang, Mengxiao, Achddou, Juliette 2026 置信度 0.66 Machine Learning (stat.ML)Artificial Intelligence (cs.AI)Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• SKIN CANCER MELONEMA DETECTION USING DEEP LEARNING- A REVIEW
  Abstract Melanoma is one of the deadliest forms of skin cancer due to its rapid progression and high metastatic potential. Early detection greatly improves survival rates, yet traditional methods relying on dermatologist expertise are subjective and prone to e…
  datacite Tanushri Jhod, Seema Kirar 2025 置信度 0.66
• SKIN CANCER MELONEMA DETECTION USING DEEP LEARNING- A REVIEW
  Abstract Melanoma is one of the deadliest forms of skin cancer due to its rapid progression and high metastatic potential. Early detection greatly improves survival rates, yet traditional methods relying on dermatologist expertise are subjective and prone to e…
  datacite Tanushri Jhod, Seema Kirar 2025 置信度 0.66
• FedPOD: the deployable units of training for federated learning
  This paper proposes FedPOD, which ranked first in the 2024 Federated Tumor Segmentation (FeTS) Challenge, for optimizing learning efficiency and communication cost in federated learning among multiple clients. Inspired by FedPIDAvg, we define a round-wise task…
  datacite Kim, Daewoon, Yie, Si Young, Lee, Jae Sung 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
• Top 5 Emerging AI Trends Transforming Digital Health in 2024
  Discover the top 5 AI trends in digital health for 2024, including federated learning, multimodal AI, edge AI, agentic AI, and quantum AI transforming healthcare.
  datacite Rasit Dinc 2025 置信度 0.66 AI in HealthcareDigital HealthFederated LearningMultimodal AIQuantum AI
• Top 5 Emerging AI Trends Transforming Digital Health in 2024
  Discover the top 5 AI trends in digital health for 2024, including federated learning, multimodal AI, edge AI, agentic AI, and quantum AI transforming healthcare.
  datacite Rasit Dinc 2025 置信度 0.66 AI in HealthcareDigital HealthFederated LearningMultimodal AIQuantum AI
• Optimization with Access to Auxiliary Information
  We investigate the fundamental optimization question of minimizing a target function $f$, whose gradients are expensive to compute or have limited availability, given access to some auxiliary side function $h$ whose gradients are cheap or more available. This …
  datacite Chayti, El Mahdi, Karimireddy, Sai Praneeth 2022 置信度 0.66 Machine Learning (cs.LG)Optimization and Control (math.OC)FOS: Computer and information sciencesFOS: Computer and information sciencesFOS: Mathematics
• Accessibility for academic teaching staff (Report 05/2025)
  The report describes a pilot “Video course on accessibility” created by Wrocław University of Science and Technology (Wrocław Tech) for academic teaching staff, including laboratory instructors. The course links the university’s local Moodle‑based LMS (ePortal…
  datacite Forycka, Renata, Herczak-Ciara, Agnieszka, Jach, Katarzyna, Krysiak, Jarosław 等 2025 置信度 0.66 Educational sciencesFOS: Educational sciencesEngineering and technologyFOS: Engineering and technologylearning management
• Accessibility for academic teaching staff (Report 05/2025)
  The report describes a pilot “Video course on accessibility” created by Wrocław University of Science and Technology (Wrocław Tech) for academic teaching staff, including laboratory instructors. The course links the university’s local Moodle‑based LMS (ePortal…
  datacite Forycka, Renata, Herczak-Ciara, Agnieszka, Jach, Katarzyna, Krysiak, Jarosław 等 2025 置信度 0.66 Educational sciencesFOS: Educational sciencesEngineering and technologyFOS: Engineering and technologylearning management
• Artificial intelligence and its application in clinical microbiology
  Traditional microbiological diagnostics face challenges in pathogen identification speed and antimicrobial resistance (AMR) evaluation. Artificial intelligence (AI) offers transformative solutions, necessitating a comprehensive review of its applications, adva…
  datacite Mairi, Assia, Hamza, Lamia, Touati, Abdelaziz 2025 置信度 0.66 Space ScienceMedicineMicrobiologyFOS: Biological sciencesBiotechnology
• Artificial intelligence and its application in clinical microbiology
  Traditional microbiological diagnostics face challenges in pathogen identification speed and antimicrobial resistance (AMR) evaluation. Artificial intelligence (AI) offers transformative solutions, necessitating a comprehensive review of its applications, adva…
  datacite Mairi, Assia, Hamza, Lamia, Touati, Abdelaziz 2025 置信度 0.66 Space ScienceMedicineMicrobiologyFOS: Biological sciencesBiotechnology
• Privacy-Preserving Machine Learning: Techniques, Frameworks, and Future Directions
  Machine Learning that Preserves Privacy (PPML) facilitates model training and analysis while safeguarding sensitive data, model parameters, and user privacy. This survey reviews advances from 2019–2024 with a focus on four major techniques: Homomorphic Encrypt…
  datacite Ruksar Fatima, Ayesha Siddiqua, Aliza Mahvash, Syeda Sheeba 2025 置信度 0.66
• Privacy-Preserving Machine Learning: Techniques, Frameworks, and Future Directions
  Machine Learning that Preserves Privacy (PPML) facilitates model training and analysis while safeguarding sensitive data, model parameters, and user privacy. This survey reviews advances from 2019–2024 with a focus on four major techniques: Homomorphic Encrypt…
  datacite Ruksar Fatima, Ayesha Siddiqua, Aliza Mahvash, Syeda Sheeba 2025 置信度 0.66
• Resource Management in the Edge–Cloud Continuum: Trends, Algorithms, and Open Challenges
  The continuum of edge and cloud computing has emerged as a vital computing model for enabling latency-sensitive, data-heavy, and geographically scattered applications. As billions of devices generate massive volumes of data, efficient resource management acros…
  datacite Ruksar Fatima, Suhana Anjum, Shaista Fatima Junaidi, Ruqayya Rafa 2025 置信度 0.66
• Resource Management in the Edge–Cloud Continuum: Trends, Algorithms, and Open Challenges
  The continuum of edge and cloud computing has emerged as a vital computing model for enabling latency-sensitive, data-heavy, and geographically scattered applications. As billions of devices generate massive volumes of data, efficient resource management acros…
  datacite Ruksar Fatima, Suhana Anjum, Shaista Fatima Junaidi, Ruqayya Rafa 2025 置信度 0.66
• Federated Learning: Advances, Privacy Mechanisms, and Real-World Deployments
  Federated Learning (FL) has emerged as a powerful paradigm that enables collaborative model training across decentralized clients while preserving data privacy. Instead of aggregating sensitive data in a central server, FL coordinates local training on distrib…
  datacite Ruksar Fatima, Aliza Mahvash, Ayesha Siddiqua, Syeda Sheeba 2025 置信度 0.66
• Federated Learning: Advances, Privacy Mechanisms, and Real-World Deployments
  Federated Learning (FL) has emerged as a powerful paradigm that enables collaborative model training across decentralized clients while preserving data privacy. Instead of aggregating sensitive data in a central server, FL coordinates local training on distrib…
  datacite Ruksar Fatima, Aliza Mahvash, Ayesha Siddiqua, Syeda Sheeba 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
• Adaptive Self-Distillation for Minimizing Client Drift in Heterogeneous Federated Learning
  Federated Learning (FL) is a machine learning paradigm that enables clients to jointly train a global model by aggregating the locally trained models without sharing any local training data. In practice, there can often be substantial heterogeneity (e.g., clas…
  datacite Yashwanth, M, Nayak, Gaurav Kumar, Singh, Arya, Simmhan, Yogesh 等 2023 置信度 0.66 Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• D3.2 Competence Centres concepts and activities (pre)existing in EOSC
  This deliverable presents an overview of EOSC-related activities and projects that could be taken into account for the design and implementation of Competence Centres (CCs), positioned as key instruments to support data-intensive, FAIR-compliant, and interdisc…
  datacite David, Romain, Hienola, Anca, Schmidt-Tremmel, Friederike, van der Lek, Iulianna 等 2025 置信度 0.66
• D3.2 Competence Centres concepts and activities (pre)existing in EOSC
  This deliverable presents an overview of EOSC-related activities and projects that could be taken into account for the design and implementation of Competence Centres (CCs), positioned as key instruments to support data-intensive, FAIR-compliant, and interdisc…
  datacite David, Romain, Hienola, Anca, Schmidt-Tremmel, Friederike, van der Lek, Iulianna 等 2025 置信度 0.66
• Minimizing Layerwise Activation Norm Improves Generalization in Federated Learning
  Federated Learning (FL) is an emerging machine learning framework that enables multiple clients (coordinated by a server) to collaboratively train a global model by aggregating the locally trained models without sharing any client's training data. It has been …
  datacite Yashwanth, M, Nayak, Gaurav Kumar, Rangwani, Harsh, Singh, Arya 等 2025 置信度 0.66 Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences