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• Safeguarding Federated Learning-Based Road Condition Classification
  crossref Sheng Liu, Panos Papadimitratos 2025-10-15T17:34:49Z 置信度 0.70
• Selective Forgetting in Hierarchical Federated Learning: Towards Global Model Unlearning of Client Data
  crossref Afsaneh Afzali, Pirooz Shamsinejad 2025-12-24T16:36:52Z 置信度 0.70
• FedRD: Personalized Federated Learning via Representation Distillation
  crossref Shuaishuai Zhang, Jie Huang, Peihao Li 2025-09-26T17:34:55Z 置信度 0.70
• Also of Interest
  crossref 2025-12-15T10:17:52Z 置信度 0.70
• Inference Attacks on FL
  crossref Somanath Tripathy, Harsh Kasyap, Minghong Fang 2025-11-21T17:09:27Z 置信度 0.70
• Federated Learning Mlops Tools. A Systematic Mapping Study
  crossref Ari Kukkaro, Sergio Moreschini, Davide Taibi, David H¨astbacka 2025-07-10T12:42:04Z 置信度 0.70
• Energy Efficient Secure Edge Intelligence with Sparse Federated Learning for Iomt
  crossref Suresh Chavhan, Emy Santo, Deepak Gupta 2025-07-14T00:22:26Z 置信度 0.70
• Secure and Efficient Federated Learning for Predictive Modeling in Resource-Constrained Healthcare Systems
  crossref Alex Mirugwe, Juwa Nyirenda 2025-08-25T17:39:32Z 置信度 0.70
• A Review of Lightweight Multi-Party Computation and Federated Learning in Financial Systems
  As the financial services are increasingly moving to the edge devices, safeguarding these sensitive transaction data without compromising on privacy has become a challenge. This systematic literature review analyzes peer-reviewed studies that explore lightweig…
  crossref Tonsia Treesa Thomas, Heta Shukla 2025-08-24T19:03:16Z 置信度 0.70
• Starlit: Privacy-Preserving Federated Learning to Enhance Financial Fraud Detection
  crossref Aydin Abadi, Mohammad Naseri, Bradley Doyle, Francesco Gini 等 2026-01-20T20:38:34Z 置信度 0.70
• Adaptive Federated Learning for Anomaly Detection in Satellite Telemetry
  This paper presents a streamlined Federated Learning (FL) framework for anomaly detection in satellite telemetry, addressing limitations of centralized approaches for predictive maintenance in resource-constrained satellite networks. Evaluating FL models on th…
  crossref Azade Atefrad, Amin Karami 2025-10-29T05:48:03Z 置信度 0.70
• Harnessing machine learning and deep learning techniques for neuroimaging
  crossref Hina Bansal, Neetu Jabalia, Kritika Shukla, Yash Nautiyal 2025-03-20T10:28:47Z 置信度 0.70
• Federated Learning for Privacy-Preserving Health Monitoring: Enhancing Data Utility While Maintaining Security
  The explosive growth of personalized health data generated by wearable devices and edge computing requires robust analytical methods that respect stringent privacy regulations (e.g., HIPAA, GDPR). Traditional centralized data aggregation poses significant secu…
  crossref CK Gomathy, Vardinni Reddii 2025-11-29T17:53:16Z 置信度 0.70
• Federated Learning with Cross-Device Transfer Learning in IoT
  crossref Jatin Aggrawal, Hariharasitaraman. S, Ajay Kumar Phulre, Irfan Alam 2025-12-19T18:56:35Z 置信度 0.70
• Data-Bound Adaptive Federated Learning: FedAdaDB
  Federated Learning (FL) enables decentralized Machine Learning (ML), focusing on preserving data privacy, but faces a unique set of optimization challenges, such as dealing with non-IID data, communication overhead, and client drift. Adaptive optimizers like A…
  crossref Fotios Zantalis, Grigorios Koulouras 2025-06-24T10:44:41Z 置信度 0.70
• Enhancing Data Privacy in 5G/6G Telecom Networks: Federated Learning as a Secure AI Framework
  The rapid evolution of 5G and the emerging 6G networks is revolutionizing the telecommunications landscape by enabling ultra-reliable low-latency communications (URLLC), massive machine-type communications (mMTC), and enhanced mobile broadband (eMBB). However,…
  crossref Itunuoluwa Adegbola 2025-09-05T11:33:48Z 置信度 0.70
• Trustworthiness Verification for Federated Learning in Web3.0 Healthcare Communities
  crossref Tianxin Wang 2026-01-29T21:19:40Z 置信度 0.70
• Federated Learning for Low-Latency IoT Communications
  crossref Mohammed M Alenazi 2025-05-21T17:36:26Z 置信度 0.70
• Robust Contribution Measurement for Federated Learning
  crossref Junyu Han 2025-10-01T17:37:15Z 置信度 0.70
• Quantifying and Analyzing Client Data Heterogeneity in Federated Learning via Multi Modal Divergence Metrics
  Federated Learning (FL) enables collaborative model training across distributed clients while preserving data privacy. However, the inherent heterogeneity in client data distributions poses significant challenges to FL performance, affecting model convergence …
  crossref Praveer Dubey, Mohit Kumar 2025-06-19T19:15:30Z 置信度 0.70
• Poisoning Attacks on FL
  crossref Somanath Tripathy, Harsh Kasyap, Minghong Fang 2025-11-21T17:09:27Z 置信度 0.70
• Fed-DiTTab: Diffusion Transformer for Tabular Data Generation in Federated Learning
  crossref Yajun Pi, Ming Zheng, Fanhao Ma 2025-11-22T15:43:02Z 置信度 0.70
• Review for "FedDW: An Adaptive Weight Aggregation Federated Learning Framework for Multi-Institutional Collaborative Polyp Segmentation"
  crossref 2025-12-31T23:57:44Z 置信度 0.70
• FedCOT: Personalized Federated Transfer Learning with Conditional Optimal Transport for Manufacturing Predictive Modeling
  Effective predictive modeling in large-scale manufacturing is hampered by the isolated and limited data from individual organizations, collected from costly experiments and various inspections. Collaboration across organizations can handle these limitations, b…
  crossref Anyi Li, Jia "Peter" Liu 2025-10-17T13:03:38Z 置信度 0.70
• Review for "FedDW: An Adaptive Weight Aggregation Federated Learning Framework for Multi-Institutional Collaborative Polyp Segmentation"
  crossref 2025-12-31T23:57:44Z 置信度 0.70
• Index
  crossref 2025-12-15T10:17:52Z 置信度 0.70
• Feature Reconstruction Aided Federated Learning for Image Semantic Communication
  crossref Yoon Huh, Bumjun Kim, Wan Choi 2026-03-19T20:04:01Z 置信度 0.70
• FedHO: Memory-Efficient Federated Fine-Tuning for Large Models via Hybrid Gradient Computation
  crossref Yinan Zhang, Jiannong Cao, Mingjin Zhang, Ruosong Yang 2025-12-02T17:01:43Z 置信度 0.70
• Toward Secure IoT Infrastructure: Integrating Zero Trust, Federated Learning, and Dynamic Trust Management Models
  With the proliferation of Internet of Things (IoT) devices across industrial, healthcare, and consumer domains, the demand for secure, scalable, and intelligent infrastructure has surged. This paper presents an in-depth synthesis of 29 high-impact research con…
  crossref Vamsi Krishna Kokku 2025-06-12T04:41:52Z 置信度 0.70
• Robust and Privacy-Preserving Feature Selection: A Permutation-Invariant Representation Learning Approach with Federated Extension
  crossref Stchingtana Naryso 2025-12-02T01:05:00Z 置信度 0.70
• Decision letter for "Breaking Interprovincial Data Silos: How Federated Learning Can Unlock Canada’s Public Health Potential"
  crossref 2026-03-14T21:10:36Z 置信度 0.70
• Privacy-Preserving Federated Learning: Balancing Accuracy and Data Protection in Distributed Machine Learning
  Problem statement. With the growing volume of sensitive data and stricter requirements for their protection, traditional centralized machine learning methods are becoming unacceptable due to the risks of leaks and breaches of confidentiality. This problem is p…
  crossref Malik A. M. Alsweity, Zlata Kim, Daniil Marshev 2025-10-09T06:58:15Z 置信度 0.70
• Compression as a Structural Precondition: Memory-Security Co-Design for Post-Quantum Federated AI at the Edge (Mnemosyne Part III)
  This work presents Part III of the Mnemosyne system design — a post-quantum secure federated learning architecture targeting deployment on 2 GB edge devices. This part establishes that compression is not a post-hoc optimisation but a structural precondition fo…
  datacite Bo Jun, Han 2026 置信度 0.66
• Compression as a Structural Precondition: Memory-Security Co-Design for Post-Quantum Federated AI at the Edge (Mnemosyne Part III)
  This work presents Part III of the Mnemosyne system design — a post-quantum secure federated learning architecture targeting deployment on 2 GB edge devices. This part establishes that compression is not a post-hoc optimisation but a structural precondition fo…
  datacite Bo Jun, Han 2026 置信度 0.66
• Machine Learning For Cloud Workload Scheduling Optimization
  As cloud computing infrastructures transition from passive resource providers to \\\"Intelligent Clouds,\\\" the complexity of managing heterogeneous, bursty, and globally distributed workloads has rendered traditional heuristic scheduling insufficient. This r…
  datacite Nimal Perera 2025 置信度 0.66
• Machine Learning For Cloud Workload Scheduling Optimization
  As cloud computing infrastructures transition from passive resource providers to \\\"Intelligent Clouds,\\\" the complexity of managing heterogeneous, bursty, and globally distributed workloads has rendered traditional heuristic scheduling insufficient. This r…
  datacite Nimal Perera 2025 置信度 0.66
• Investigating and developing efficient Federated Learning for air pollution prediction
  Air pollution has long posed a major public health threat in the region of Western Macedonia in Greece, a threat that is attributed to lignite mining and heavily polluted gases that are emitted during lignite burning to produce electricity. While modern Intern…
  datacite Evangelopoulos Georgios, Ευαγγελοπουλος Γεωργιος 2026 置信度 0.66 Federated learning (Machine learning)http://id.loc.gov/authorities/subjects/sh2024001385
• From Patterns to Policy: A Scoping Review Based on Bibliometric Analysis (ScoRBA) of Intelligent and Secure Smart Hospital Ecosystems
  This study examines the evolution of Intelligent and Secure Smart Hospital Ecosystems using a Scoping Review with Bibliometric Analysis (ScoRBA) to map research patterns, identify gaps, and derive policy implications. Analyzing 891 journal articles from Scopus…
  datacite Wijaya, Adi, Hermawan, Budi, Baihaqi, Wiga Maulana, Supriyanto, Catur 2026 置信度 0.66 Neurons and Cognition (q-bio.NC)Computers and Society (cs.CY)FOS: Biological sciencesFOS: Biological sciencesFOS: Computer and information sciences
• <b>A Comprehensive Survey on Federated Learning</b><b>and Decentralised Access Coordination for Machine</b><b>Learning. </b>Madhu Nagraj¹, Vignesh T D¹, Ranjitha¹, Ananya Raj MN¹, Bharath N¹
  Federated Learning (FL) has become a core strategy for training machine-learning models over sensitive and continuously expanding data without centralizing raw records. This survey consolidates current (2022–2025) technical directions across horizontal, vertic…
  datacite T D, Vignesh 2025 置信度 0.66 Numerical computation and mathematical software
• Federated Predictive Analytics in Finance: Privacy-Preserving Churn and Fraud Forecasting Across Institutions
  Abstract The escalating adoption of machine learning in financial services is impeded by regulatory constraints on data sharing and the fragmentation of customer data across competing institutions. This article investigates how federated learning (FL) architec…
  datacite ANGEL JOSEPH, Dency D, Abel Jopaul V P and Dr Mohammad Irshad V K 2026 置信度 0.66 federated learning, churn prediction, fraud detection, differential privacy, secure aggregation, regulatory compliance, bias reduction, financial analytics, GDPR, DORA
• Federated Predictive Analytics in Finance: Privacy-Preserving Churn and Fraud Forecasting Across Institutions
  Abstract The escalating adoption of machine learning in financial services is impeded by regulatory constraints on data sharing and the fragmentation of customer data across competing institutions. This article investigates how federated learning (FL) architec…
  datacite ANGEL JOSEPH, Dency D, Abel Jopaul V P and Dr Mohammad Irshad V K 2026 置信度 0.66 federated learning, churn prediction, fraud detection, differential privacy, secure aggregation, regulatory compliance, bias reduction, financial analytics, GDPR, DORA
• FED-HARGPT: A Hybrid Centralized-Federated Approach of a Transformer-based Architecture for Human Context Recognition
  The study explores a hybrid centralized-federated approach for Human Activity Recognition (HAR) using a Transformer-based architecture. With the increasing ubiquity of edge devices, such as smartphones and wearables, a significant amount of private data from w…
  datacite Gibaut, Wandemberg, Osorio, Alexandre, Munoz, Amparo, Neto, Sildolfo F. G. 等 2026 置信度 0.66 Signal Processing (eess.SP)Artificial Intelligence (cs.AI)Machine Learning (cs.LG)FOS: Electrical engineering, electronic engineering, information engineeringFOS: Electrical engineering, electronic engineering, information engineering
• Artificial Intelligence and Radiomics for Differentiating Pseudoprogression from True Progression in High-Grade Gliomas: A Meta-Analysis
  Differentiating true progression (TP) from pseudoprogression (PsP) in high-grade gliomas (HGGs) on MRI is a critical clinical challenge. This meta-analysis evaluates the overall diagnostic accuracy of radiomics and artificial intelligence (AI) models to identi…
  datacite Facchinetti, Giovanni, De Maria, Lucio, Pagani, Nicola, Ponzio, Francesco 2026 置信度 0.66 RadiologyMedicine and Health SciencesMedical SpecialtiesOncologyNeurology
• Multi-Modal Multi-Task (M3T) Federated Foundation Models for Embodied AI: Potentials and Challenges for Edge Integration
  As embodied AI systems become increasingly multi-modal, personalized, and interactive, they must learn effectively from diverse sensory inputs, adapt continually to user preferences, and operate safely under resource and privacy constraints. These challenges e…
  datacite Borazjani, Kasra, Abdisarabshali, Payam, Nadimi, Fardis, Khosravan, Naji 等 2025 置信度 0.66 Artificial Intelligence (cs.AI)Robotics (cs.RO)FOS: Computer and information sciencesFOS: Computer and information sciences
• Curriculum Guided Personalized Subgraph Federated Learning
  Subgraph Federated Learning (FL) aims to train Graph Neural Networks (GNNs) across distributed private subgraphs, but it suffers from severe data heterogeneity. To mitigate data heterogeneity, weighted model aggregation personalizes each local GNN by assigning…
  datacite Kang, Minku, Park, Hogun 2025 置信度 0.66 Machine Learning (cs.LG)Artificial Intelligence (cs.AI)FOS: Computer and information sciencesFOS: Computer and information sciences
• Federated Multi Agent Deep Learning and Neural Networks for Advanced Distributed Sensing in Wireless Networks
  Multi-agent deep learning (MADL), including multi-agent deep reinforcement learning (MADRL), distributed/federated training, and graph-structured neural networks, is becoming a unifying framework for decision-making and inference in wireless systems where sens…
  datacite Muller, Nadine, DeRosa, Stefano, Zhang, Su, Huan, Chun Lee 2026 置信度 0.66 Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• PROTECTING THE PRIVACY OF PROTECTED HEALTH INFORMATION (PHI) IN THE ERA OF AI-ASSISTED HEALTHCARE: A SYSTEMATIC REVIEW
  AI is being used to support many aspects of healthcare from drug discovery to AI-assisted surgery to remote monitoring of patient vitals. Because of AI’s powerful potential to do harm at scale, it is really important that guardrails and guidelines be put into …
  datacite Adams, Timothy Allen 2026 置信度 0.66 Artificial intelligence not elsewhere classified
• PROTECTING THE PRIVACY OF PROTECTED HEALTH INFORMATION (PHI) IN THE ERA OF AI-ASSISTED HEALTHCARE: A SYSTEMATIC REVIEW
  AI is being used to support many aspects of healthcare from drug discovery to AI-assisted surgery to remote monitoring of patient vitals. Because of AI’s powerful potential to do harm at scale, it is really important that guardrails and guidelines be put into …
  datacite Adams, Timothy Allen 2026 置信度 0.66 Artificial intelligence not elsewhere classified
• An Empirical Analysis of Federated Learning Models Subject to Label-Flipping Adversarial Attack
  In this paper, we empirically analyze adversarial attacks on selected federated learning models. The specific learning models considered are Multinominal Logistic Regression (MLR), Support Vector Classifier (SVC), Multilayer Perceptron (MLP), Convolution Neura…
  datacite Bhatnagar, Kunal, Chattanathan, Sagana, Dang, Angela, Eranki, Bhargav 等 2024 置信度 0.66 Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• Rethinking the Backbone in Class Imbalanced Federated Source Free Domain Adaptation: The Utility of Vision Foundation Models
  Federated Learning (FL) offers a framework for training models collaboratively while preserving data privacy of each client. Recently, research has focused on Federated Source-Free Domain Adaptation (FFREEDA), a more realistic scenario wherein client-held targ…
  datacite Kihara, Kosuke, Mori, Junki, Miyagawa, Taiki, Ebihara, Akinori F. 2025 置信度 0.66 Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• Liver Cancer Risk Prediction
  The dataset contains 465,605 visit-level records corresponding to 15,500 individuals monitored across 12 distributed healthcare nodes between January 2021 and December 2025. Each record integrates heterogeneous clinical, behavioral, physiological, imaging-deri…
  datacite Eric S., Lander 2026 置信度 0.66
• Liver Cancer Risk Prediction
  The dataset contains 465,605 visit-level records corresponding to 15,500 individuals monitored across 12 distributed healthcare nodes between January 2021 and December 2025. Each record integrates heterogeneous clinical, behavioral, physiological, imaging-deri…
  datacite Eric S., Lander 2026 置信度 0.66
• About the Editors
  crossref 2025-10-03T21:19:49Z 置信度 0.70
• Federated Learning for Geothermal Energy: A Decentralized Solution for Data Sharing and Sector Learning
  Abstract Geothermal energy faces challenges in design and operation due to subsurface uncertainties, high drilling costs, and complex maintenance requirements. Data and model sharing can enhance decision making processes in the geothermal sector by improving p…
  crossref P. Shoeibi Omrani, S. Ben Aziza 2025-06-10T02:21:01Z 置信度 0.70
• SecureBFL: a Blockchain-enhanced federated learning architecture with MPC
  crossref Tanguy Vansnick, Leandro Collier, Saïd Mahmoudi 2025-04-15T14:43:24Z 置信度 0.70
• Privacy-Preserving Mortality Prediction in ICUs Using Federated Learning
  crossref Pedro Vieira, Eva Maia, Isabel Praça 2025-02-25T06:22:30Z 置信度 0.70
• Federated Continual Learning: A Survey on Mitigating Spatial-Temporal Catastrophic Forgetting
  crossref Tianyi Xie 2025-12-20T09:12:49Z 置信度 0.70
• FedMI: Heterogeneous Federated Learning Based on Multi-Model Inference
  crossref Deyang Wan, Changbao Li 2025-08-07T17:41:45Z 置信度 0.70
• Virtual Adversarial Training (VAT)-based Secure Federated Learning Platform: Design and Evaluation
  crossref Takumi Tojo, Ryo Kumagai, Shu Takemoto, Yusuke Nozaki 等 2025-12-14T09:09:11Z 置信度 0.70
• Real-world implementation and application
  crossref An Xu, Bowen Li, Can Zhao 2025-03-07T12:49:41Z 置信度 0.70
• Federated Learning and Neural Circuit Policies: A Novel Framework for Anomaly Detection in Energy-Intensive Machinery
  In the realm of predictive maintenance for energy-intensive machinery, effective anomaly detection is crucial for minimizing downtime and optimizing operational efficiency. This paper introduces a novel approach that integrates federated learning (FL) with Neu…
  openalex Giulia Palma, Giovanni Geraci, Antonio Rizzo 2025-02-15 置信度 0.72 Anomaly detectionComputer scienceEnergy (signal processing)Anomaly (physics)Artificial intelligence
• Advanced Privacy Measures for Data Sharing in Federated Learning Networks
  crossref P. Manju Bala, S. Usharani, A. Balachandar, A. Olukayode 2025-11-07T13:28:25Z 置信度 0.70
• Collaborative Health Intelligence
  Federated Learning empowers hospitals and research centres to train AI models collaboratively without sharing patient records. It is the decentralized alternative in which AI models can be trained across multiple healthcare organizations. The patient data is s…
  crossref Nancy Soni, Asomi Chowdhury, Hina Bansal 2025-10-30T14:01:36Z 置信度 0.70
• Skin Cancer Classification and Detection Using Federated Learning
  crossref Malliga Subramanian, Kalaivani B, Jeevasree G, Mathan Kumar A 等 2025-09-26T09:33:50Z 置信度 0.70
• Federated Learning and Its Applications in Smart Agricultural Processes
  crossref Mahesh Kumar Singh, Pushpa Choudhary, Akhilesh Kumar Singh, Arun Kumar Singh 等 2025-12-15T10:17:52Z 置信度 0.70
• Federated Machine Learning Framework for Soil Classification in Smart Agriculture
  crossref Marwen Ghabi, Sofiane Khalfallah, Hela Ltifi 2025-02-28T12:43:20Z 置信度 0.70
• Cryptography Securing Data in Motion Within Blockchain, Internet of Everything, and Federated Learning
  Data protection relies on cryptography to secure data across Blockchain, IoE, and Federated Learning systems. Strong cryptographic methods ensure confidentiality, authenticity, and integrity, safeguarding evolving digital security needs. Key techniques include…
  crossref S. Aarthi, K. Aravinthan, R. N. Ravikumar, N. Sivakumar 等 2025-05-13T16:35:42Z 置信度 0.70
• Adaptive and Secure ETL for Defense Data Systems Using Federated Reinforcement Learning
  crossref Manohar Reddy Sokkula 2025-10-23T10:50:30Z 置信度 0.70
• Automation of AI and IoT-Based Data-Driven Decision-Making Approaches Using Federated Learning Systems
  crossref M. Sundarrajan, Mani Deepak Choudhry, Rajesh Kumar Dhanaraj, Mariya Ouaissa 2025-06-20T18:53:47Z 置信度 0.70
• Federated Learning for Enterprise AI
  crossref Ben Tan, Yan Kang, Lixin Fan, Vincent Zheng 2025-11-15T18:21:09Z 置信度 0.70
• Intrusion Detection System Using Federated Learning
  crossref Zarinabegum Mundargi, Atharva Bondarde, Atharva Joshi, Archit Bagad 等 2026-07-31T13:14:40Z 置信度 0.70
• Federated learning-enabled CNN for predicting and detecting brain tumors in healthcare 6.0
  crossref Boggarapu Srinivasulu, Srinivasa Rao Dhanikonda, Dammu Venkata Ravi Kumar, Ravega Venkata Gandhi 等 2025-03-20T10:28:47Z 置信度 0.70
• Pqbfl: A Post-Quantum Blockchain-Based Protocol for Federated Learning
  crossref Hadi Gharavi, Edmundo Monteiro, Jorge Granjal 2025-03-24T21:39:36Z 置信度 0.70
• FedDDF: Dynamic Dataset Filtering in Federated Large Language Model Training
  crossref Nguyen Linh Bao Nguyen, Thuan Quang Tran, Kok-Seng Wong 2025-07-15T07:07:04Z 置信度 0.70
• Personalized Federated FlowChain for Human Trajectory Prediction
  crossref Kannanthodath Induchoodan Ajay Menon, Christian Prehofer, Yunfei Xu, Sarandeep Singh 2026-01-20T20:38:34Z 置信度 0.70
• Federated Attention Autoencoders with a Stochastic Aggregation Scheme for Anomaly Detection
  crossref Mihailo Ilić, Miloš Savić, Vladimir Kurbalija, Mirjana Ivanović 等 2026-01-20T20:38:34Z 置信度 0.70
• MOON-DPAP: Model-Contrastive Federated Learning with Differential Privacy and Adaptive Pruning
  crossref Jiaming Su 2025-09-30T10:05:46Z 置信度 0.70
• Introduction
  crossref Mei Kobayashi 2025-08-01T12:37:32Z 置信度 0.70
• Real-Time Federated Learning Architecture for Privacy-Preserving Edge Intelligence
  crossref Priya Vij, Manish Nandy 2026-03-05T22:09:31Z 置信度 0.70
• Enabling Decentralized Clinical Insights
  Healthcare AI has federated learning as a transformational approach in which clinical research can be conducted collaboratively and in patient privacy is preserved. This chapter considers federated learning and how it enables healthcare institutions to learn f…
  crossref Sonam Gupta, Pradeep Gupta, Lipika Goel, Sachin Jain 2025-10-30T14:01:36Z 置信度 0.70
• Homomorphic Encryption: the Missing Piece in Federated Learning
  crossref Aurora Anna Francesca Colombo, Alessandro Falcetta, Manuel Roveri 2025-10-08T03:38:09Z 置信度 0.70
• Survey on Federated Learning UtilisingDeep Learning Models for DiverseApplications
  Survey on Federated Learning Utilising Deep Learning Models for Diverse Applications
  crossref NANDAKUMAR |M, RANJITH N 2025-08-08T11:26:00Z 置信度 0.70
• Federated Learning for BYOD-Enabled Mobile Mentorship: Ensuring Privacy in Cloud-Telecom Converged Networks
  crossref Thaker Bhavik 2025-09-12T11:53:57Z 置信度 0.70
• Privacy-Preserving Federated Learning for IoT Botnet Detection: A Federated Averaging Approach
  Traditional centralized machine learning approaches for IoT botnet detection pose significant privacy risks, as they require transmitting sensitive device data to a central server. This study presents a privacy-preserving Federated Learning (FL) approach that …
  crossref Praveen Kumar Myakala, Srikanth Kamatala, Chiranjeevi Bura 2025-07-25T15:30:27Z 置信度 0.70
• AI-Driven Energy Optimization for Smart Cities: Federated Reinforcement Learning and Digital Twin Framework
  This study presents a novel artificial intelligence (AI)-based framework for optimizing energy management in smart cities, integrating attention-based long short-term memory (LSTM) networks, reinforcement learning (RL), and privacypreserving federated learning…
  crossref Arvindh Balajie Sundararajan 2025-11-10T21:45:59Z 置信度 0.70
• Towards Scalable and Secure Federated Learning: Key Issues and Future Research
  Federated Learning (FL) is a decentralized machine learning approach that enables collaborative model training across distributed data sources while ensuring data privacy. Unlike traditional centralized approaches, FL allows multiple clients (e.g., mobile devi…
  crossref Marilyn Daniels, Sameera Gallus, Rebekah Wood 2025-02-07T12:13:53Z 置信度 0.70
• Relayfl:Advancing Federated Learning Towards Client Heterogeneity
  crossref yang yong, shaoshuai gao, tingting yang, jiahong ning 等 2025-06-21T15:41:53Z 置信度 0.70
• Front Matter
  crossref 2025-12-15T10:17:52Z 置信度 0.70
• CASCADENCE: A Layered Cascade Defense Mechanism for Federated Learning
  This paper aims to enhance the security and robustness of Federated Learning (FL) systems through a multilayered defense. We address the critical challenge of protecting distributed learning environments from adversarial attacks while maintaining high model pe…
  crossref Syed Waquas Hashmi, Raj Mani Shukla, Suman Bhunia 2025-11-27T06:27:07Z 置信度 0.70
• Towards Decentralized Dental Diagnostics: Federated Learning for Caries Detection
  crossref Ruchika Das, Shobhanjana Kalita 2025-09-26T17:35:05Z 置信度 0.70
• The Impact of Federated Learning on Distributed Remote Sensing Archives
  When it comes to Machine Learning in remote sensing, one of the main obstacles researchers face is the large scale of datasets. Just the size of freely available Earth observation data presents a challenge for personal computers. A variety of missions, such as…
  crossref Vijay Govindarajan, Pratik Surendra Kumar Patel 2025-06-20T14:56:21Z 置信度 0.70
• Real-Time Financial Fraud Detection Using Adaptive Graph Neural Networks and Federated Learning
  Abstract Detecting financial fraud in real time is an ongoing challenge due to the ever-evolving nature of fraudulent activities. Conventional fraud detection systems rely heavily on static machine learning models, which often struggle to adapt to emerging fra…
  crossref Milad Rahmati 2025-02-17T06:34:56Z 置信度 0.70
• Federated Learning for UAV Perception Task : A Survey
  Unmanned Aerial Vehicles (UAVs) are increasingly deployed for perception tasks such as surveillance, object detection, and traffic monitoring, which play a crucial role in intelligent vehicle systems. Federated Learning (FL) offers a decentralized framework th…
  crossref Yanis Bardes, Hassan Soubra, Zineb Noumir, Amar Ramdane-Cherif 2025-06-04T14:24:55Z 置信度 0.70
• Federated Learning for Text Classification: Reflections on Model Selection and Data Distribution
  crossref Tianyang Fu 2025-12-20T09:12:49Z 置信度 0.70
• Privacy-Preserving Personalized Federated Prompt Learning for Vision-Language Models
  crossref Yinan Wu, Yanli Ren, Mu Huang 2025-04-15T16:37:14Z 置信度 0.70
• Relayfl:Advancing Federated Learning Towards Clientheterogeneity Relayfl
  crossref yang yong, tingting yang, shaoshuai gao, jiahong ning 等 2025-04-07T15:47:20Z 置信度 0.70
• Blockchain Federated Learning in the Internet of Medical Things with Edge Computing: Frameworks and Approaches
  Federated Learning (FL) combined with edge computing has transformed the healthcare sector by utilizing medical data from edge devices and enabling improved real-time patient monitoring, diagnosis, and treatment. However, medical data has strict privacy requir…
  crossref Athena Rahmatie 2025-10-06T15:10:06Z 置信度 0.70
• Federated Learning and Hybrid IDS: A Novel Approach to IoT Security
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