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• Application-Driven Taxonomy of Security and Privacy Threats in Federated Learning
  preprints 2025 置信度 0.74
• FedCKD: Cluster-Aware Knowledge Distillation for Heterogeneous Medical Federated Learning
  Abstract In medical knowledge systems, federated learning provides a promising paradigm for collaborative knowledge extraction while preserving data privacy. However, inherent heterogeneity in medical information—stemming from variations in disease distributio…
  preprints Yi Xu, Kun Chen, Haoyu Luo, Xiao Liu 2025 置信度 0.74
• A Novel Neural Network-Based Federated Learning System for Imbalanced and Non-IID Data
  preprints 2025 置信度 0.74
• Privacy-Preserving Hierarchical Fog Federated Learning (PP-HFFL) for IoT Intrusion Detection
  preprints 2025 置信度 0.74
• Federated Learning for Healthcare Data Privacy: A Case Study in Multi-Hospital Collaboration
  preprints 2025 置信度 0.74
• Flexible and Scalable Federated Learning with Deep Feature Prompts for Digital Pathology
  preprints 2025 置信度 0.74
• Scalable Multi-Party Collaborative Data Mining Based on Federated Learning
  preprints 2025 置信度 0.74
• FedIHRAS: A Privacy-Preserving Federated Learning Framework for Multi-Institutional Collaborative Radiological Analysis with Integrated Explainability and Automated Clinical Reporting
  preprints 2026 置信度 0.74
• HyBloFED: A Hybrid Blockchain Integrated Federated Learning Approach for Brain Tumor Classification
  Abstract Brain tumors, complex and potentially devastating, demand precise classification for effective patient prognosis and treatment planning. This paper introduces a novel approach to automate brain tumor classification using deep learning techniques, part…
  preprints Bela Shrimali, Sarthak Joshi, Hiren Patel 2025 置信度 0.74
• Hierarchical Personalized Continual Federated Learning for Real Time Risk Prediction of Chronic Diseases
  Abstract The most prevalent global morbidity and mortality is chronic illnesses like cardiovascular diseases, diabetes, and respiratory diseases which require the proper prediction of risks and in a timely manner so as to have preventative measures. Neverthele…
  preprints Abhigyan Ghoshal, Mohammad Armaan Ali, M. Sambath, E. Balraj 2025 置信度 0.74
• Federated Learning for Agentic Gen AI in Financial Risk Management for National Financial Security
  preprints 2025 置信度 0.74
• Adaptive Trust-Driven Federated Learning with Blockchain for Secure AI Healthcare Diagnostics
  preprints 2025 置信度 0.74
• Population-Scale Developmental Risk Screening from Retail Transaction Data Using Privacy-Preserving Federated Learning
  preprints 2025 置信度 0.74
• Quantum-Safe Federated Learning with CORS-Secured APIs for Rapid Food Safety Hazard Dashboards in React SPAs
  europepmc 2025 置信度 0.80
• Optimal Uncertainty Budget Allocation for Robust Federated Learning under Byzantine Attacks
  Abstract Federated learning (FL) has revolutionized the development of machine learning models by enabling decentralized training while safeguarding user privacy. However, the presence of Byzantine adversaries introduces significant vulnerabilities, as malicio…
  preprints Weiwei Lian, Jun Tao, Xinjun Mei, Yu Fang 等 2025 置信度 0.74
• Federated Learning-Based Trust and Energy-Aware Routing in Fog–Cloud Computing Environments for the Internet of Things
  europepmc 2025 置信度 0.80
• A Federated Learning-based Optic Disc and Cup Segmentation Model for Glaucoma Monitoring In Color Fundus Photographs
  preprints 2025 置信度 0.74
• Towards a Technology-Enabled Unified Educational Space in Central Asia: A Chinese Perspective on Blockchain, Federated Learning, and Neural Machine Translation
  preprints 2026 置信度 0.74
• Enhanced Security Verifiable Secure Aggregation Scheme in Federated Learning
  Abstract Federated Learning(FL) enables multiple participants to build a loosely coupled distributed machine learning system under the coordination of a central server. Existing FL models typically assume that the server aggregating data is semi-honest, but th…
  preprints Wujun Yao, Yiliang Han, Tanping Zhou, Xiaolin Wang 2025 置信度 0.74
• Medical support platform for melanoma analysis and detection based on Federated Learning
  Abstract Advances in computer science and medicine have led to the emergence of artificial intelligence as a key tool in the medical and scientific fields. Its application in the diagnosis and treatment of diseases, such as cancer, has proven to be fundamental…
  preprints Sergio Laso, Juan Luis Herrera, Daniel Flores-Martin 2025 置信度 0.74
• Federated Learning-Driven Health Risk Prediction on Electronic Health Records Under Privacy Constraints
  preprints 2025 置信度 0.74
• Federated Learning for Power Cyber-Physical Systems: Toward Secure, Resilient, and Explainable Intelligence
  The digital transformation of power cyber-physical systems (CPSs) introduces unprecedented opportunities for optimization, forecasting, and real-time control, while simultaneously exposing critical vulnerabilities in data security, system resilience, and opera…
  preprints Zhiye Wang 2025 置信度 0.74
• A Financial Multimodal Sentiment Analysis Model Based on Federated Learning
  preprints 2025 置信度 0.74
• Privacy-Preserving Text Summarization through Differentially Private Federated Learning with Transformer Models
  Abstract As the need for safe and effective information processing grows, text summarization has become a crucial Natural Language Processing (NLP) application field. In this paper, the new Transformer based privacy-preserving summarisation system is presented…
  preprints Umme Sara, Md Tanjum An Tashrif 2025 置信度 0.74
• FMed-Diffusion Federated Learning on Medical Image Diffusion
  Abstract Medical data is not available for public access due to privacy concerns of the patients and the stakeholders’ trust-worthiness. However, Artificial Intelligence, especially all deeplearning models, is data-hungry and fails to produce clinically releva…
  preprints Murukessan Perumal, M Srinivas 2025 置信度 0.74
• AI-Driven Raman Spectroscopy and Quantum Dot Probes with Federated Learning for Micro-Nanoplastic Detection and Removal in Livestock and Aquaculture Feeds
  Micro and nanoplastics, pervasive environmental pollutants smaller than 5 mm and 1 µm respectively, infiltrate livestock and aquaculture feeds via contaminated water, sewage sludge fertilizers, and atmospheric deposition, compromising animal health, reproducti…
  preprints 2026 置信度 0.74
• A Collision-Aware Optimization Framework for Wireless Federated Learning over Grant-Free NOMA
  preprints 2025 置信度 0.74
• Enhancing Federated Learning Performance under Poor Network Conditions through a Modified UDP Protocol in the NS-3
  preprints 2025 置信度 0.74
• Cyst-X: AI-Powered Pancreatic Cancer Risk Prediction from Multicenter MRI in Centralized and Federated Learning
  preprints 2025 置信度 0.74
• Federated Learning for Secure and Privacy- Preserving Edge AI in Smart Cities
  Abstract The rapid expansion of smart cities has led to the integration of Artificial Intelligence (AI) at the edge, enabling real-time decision-making for intelligent urban infrastructure. However, conventional centralized AI models pose critical challenges, …
  preprints Joshi, Shahin Fatima, Kesani Hanirvesh, Shadab Siddiqui 等 2025 置信度 0.74
• Federated Learning with Adversarial Optimisation for Secure and Efficient 5G Edge Computing Networks
  preprints 2025 置信度 0.74
• Quantization-Based Chained Privacy-Preserving Federated Learning
  Abstract Federated Learning (FL) is an advanced distributed machine learning framework crucial in protecting data privacy and security. By enabling multiple participants to train models while keeping their data local collaboratively, FL effectively mitigates t…
  preprints Ya Liu, Shumin Wu, Yibo Li, Fengyu Zhao 等 2025 置信度 0.74
• Secure and Efficient Federated Learning for Predictive Modeling in Resource-Constrained Healthcare Systems
  preprints 2025 置信度 0.74
• Resisting Against  Targeted Poisoning Attacks in Lightweight Privacy-Preserving Federated Learning
  Abstract Federated learning is a distributed computing paradigm designed to protect client privacy. However, its distributed nature makes it vulnerable to targeted poisoning attacks.Although existing solutions can effectively mitigate such attacks, they often …
  preprints Hongliang Zhang, Haojie Xie, Jiandong Lv 2025 置信度 0.74
• EcoFedX- Adaptive Multi-Objective Federated Learning for Energy- Efficient Image and Speech Signal Processing at the Edge 
  preprints 2025 置信度 0.74
• Federated Learning for Heterogeneous Data Integration and Privacy Protection
  preprints 2025 置信度 0.74
• FL-FAITH: A Hybrid MCDM-DDPG Framework for Multi-Objective Client Selection in Federated Learning
  Abstract Client selection significantly influences the overall performance, convergence behavior, and resource utilization of Federated Learning (FL), particularly under circumstances typified by device heterogeneity and resource limitations. Conventional sele…
  preprints Tushar Mane, Shraddha Phansalkar 2025 置信度 0.74
• Verifiable Secure Aggregation Scheme for Privacy Protection in Federated Learning
  Abstract Federated learning enables multiple participants to construct a distributed machine learning system coordinated by server. Most existing solutions assume a semi-honest system, considering each participant to be honest but curious, which does not align…
  preprints Wujun Yao, Tanping Zhou, Yiliang Han, Xiaolin Wang 2025 置信度 0.74
• Federated Learning for Multi-Disease Ophthalmic Diagnostics using OCTA
  preprints 2025 置信度 0.74
• DEFEND: Intelligent Temporal Backdoor Detection and Mitigation in Federated Learning via Reinforcement Learning-Coordinated Multi-Layer Defense
  Abstract Collaborative machine learning in financial systems faces an escalating security threat: temporal backdoor attacks that exploit multi-round dependencies to systematically compromise fraud detection and risk assessment models—a challenge that existing …
  europepmc Wenan Liu, Qixuan Yang, Weihang Gong, Rongji Yin 等 2025 置信度 0.80
• Fair Federated Learning
  Optimization is critical in various fields like smart vehicles, and transportation. Federated Learning (FL) has emerged as an effective approach in the coordination of autonomous vehicles, but traditional methods such as FedAvg can create performance dispariti…
  preprints Amirreza Talebi 2024 置信度 0.74
• Federated Learning for ICU Mortality Prediction: Balancing Accuracy and Privacy in a Multi-Hospital Setting
  preprints 2025 置信度 0.74
• An Efficient Collusion-Resistant and Drop-Proof Federated Learning Security Aggregation Scheme Based on RLWE
  europepmc 2025 置信度 0.80
• A Robust Federated Learning Against Data Poisoning Attacks: Prevention and Detection of Attacked Nodes
  preprints 2025 置信度 0.74
• Federated learning enabled privacy-preserving data access for predicting 30-day mortality in acute myocardial infarction
  preprints 2025 置信度 0.74
• FL-P4AV: A Federated Learning-Based Privacy-Preserving Personalized Path Planning Framework for Collaborative Autonomous Ground Vehicles
  Abstract The growing deployment of autonomous ground vehicles in smart cities and logistics demands secure, efficient, and context-aware navigation systems. This paper proposes FL-P4AV, a Federated Learning-Enabled Privacy-Preserving Personalized Path Planning…
  preprints Saranya C, Janaki G 2025 置信度 0.74
• HDFedAtt-IIoT: A Novel Privacy-Preserving Hybrid Deep Federated Learning Framework with Attention and Proximal Regularization for IIoT Systems
  preprints 2025 置信度 0.74
• FL-P4AV: A Federated Learning-Based Privacy-Preserving Personalized Path Planning Framework for Collaborative Autonomous Ground Vehicles
  Abstract The growing deployment of autonomous ground vehicles in smart cities and logistics demands secure, efficient, and context-aware navigation systems. This paper proposes FL-P4AV, a Federated Learning-Enabled Privacy-Preserving Personalized Path Planning…
  preprints Saranya C, Janaki G 2025 置信度 0.74
• Sparse Federated Learning-Enabled Multimodal Human-Centric Traffic Systems: Resolving Privacy and Cross-Regional Adaptation Issues in ITS
  preprints 2025 置信度 0.74
• Smart IoT Anomaly Detection Using DO-TAO (Dandelion Optimization with T-distribution and Adaptive Opposition) and Personalized Federated Learning
  preprints 2025 置信度 0.74
• A Robust Federated Learning Against Data Poisoning Attacks: Prevention and Detection of Attacked Nodes
  preprints 2025 置信度 0.74
• Federated Learning-Enabled Secure Multi-Modal Anomaly Detection for Wire Arc Additive Manufacturing
  preprints 2025 置信度 0.74
• Privacy-preserving predictive maintenance method for cross-border unmanned logistics system integrating federated learning and blockchain
  Abstract Predictive maintenance of cross-border unmanned logistics systems (CBULS) faces multiple challenges such as data privacy protection, system performance optimization, and collaborative efficiency. To solve these problems, this paper proposes a predicti…
  europepmc Qingzhen Meng 2025 置信度 0.80
• Decentralized Data Governance and Regulatory Compliance in Federated Learning and Edge Computing for Healthcare
  europepmc 2025 置信度 0.80
• BlockFed: Blockchain-based Privacy Preserving Federated Learning for 5G-assisted Healthcare Ecosystems
  Abstract With the rapid adoption of 5G networks and the growing reliance on digital healthcare, the need for secure, efficient, and privacy-aware data processing has become increasingly critical. This paper presents a novel approach BlockFed , that integrates …
  europepmc Ashwin Verma, Sunil Pathak, Pronaya Bhattacharya 2025 置信度 0.80
• A Survey of Recent Advances for Tackling Data Heterogeneity in Federated Learning
  preprints 2025 置信度 0.74
• Multi-Layer Defense Strategies and Privacy Preserving Enhancements for Membership Reasoning Attacks in a Federated Learning Framework
  preprints 2025 置信度 0.74
• Federated Learning for XSS Detection: Analysing OOD, Non-IID Challenges, and Embedding Sensitivity
  preprints 2025 置信度 0.74
• EnDuSecFed: An Ensemble approach for Privacy Preserving Federated Learning with Dual-security Framework for Healthcare Domain
  preprints 2025 置信度 0.74
• Application and Effectiveness Evaluation of Federated Learning Methods in Anti-Money Laundering Collaborative Modeling Across Inter-Institutional Transaction Networks
  preprints 2025 置信度 0.74
• Federated Learning in Edge Computing: Vulnerabilities, Attacks, and Defenses-A Survey.
  pubmed Alhawas S, Rassam MA 2026 置信度 0.82
• A hierarchical federated learning framework with FedNova, game-theoretic matching, and QKD-assisted privacy for the internet of vehicles.
  The Internet of Vehicles (IoV) supports essential intelligent transportation applications but encounters challenges in federated learning (FL) due to non-independent and identically distributed (non-IID) data, vehicle mobility, resource heterogeneity, and stri…
  pubmed Jai Vinita L, Vetriselvi V 2026 置信度 0.82
• GA-AFedOD: gradient-aligned active federated learning for resource-aware object detection in edge industrial IoT.
  Visual object detection is essential for defect inspection and process monitoring in edge-deployed Industrial Internet of Things (IIoT). Yet, training accurate detectors across distributed factories faces stringent constraints on data privacy, annotation budge…
  pubmed Wang Z, Yuan X, Chen J 2026 置信度 0.82
• Federated learning framework for medical image analysis with perspective-aware contrastive and mixture of experts.
  Medical image analysis faces persistent challenges due to the distributed data, limited annotations, and variations in imaging modalities, acquisition protocols, and patient demographics. Centralized deep learning approaches compromise data privacy, while Fede…
  pubmed Das S, Hemalatha K 2026 置信度 0.82
• CONCORDAT: Trustworthy multimodal federated learning for autism research under incomplete, heterogeneous and privacy-constrained health data
  Abstract Multimodal brain-health research combines imaging-derived measurements with demographic, phenotypic and behavioural information, but these data are often incomplete, heterogeneous across sites, and subject to governance constraints that prevent subjec…
  europepmc Jarin Alam Prity 2026 置信度 0.80
• Poisoning Attacks in Federated Learning: An Accountability-Oriented Survey with Centralized Learning as Baseline
  Artificial intelligence (AI) systems are increasingly deployed in critical domains such as healthcare, finance, defense, and transportation. These deployments, however, face grow- ing risks from poisoning attacks that corrupt training data, manipulate model up…
  europepmc Safiia Mohammed, Dima Alhadidi, Alioune Ngom 2026 置信度 0.80
• Decentralized Federated Learning With Distributed Aggregation Weight Optimization.
  europepmc 2026 置信度 0.80
• Multimodal Federated Learning in Healthcare: A Review.
  Recent advancements in multimodal machine learning have empowered the development of accurate and robust AI systems in the medical domain, especially within centralized database systems. Simultaneously, Federated Learning (FL) has progressed, providing a decen…
  pubmed Thrasher J, Devkota A, Siwakoti P, Chivukula R 等 2026 置信度 0.82
• Towards Federated Learning Across Biobanks: Prototype Software from the 2026 Carnegie Mellon University–NVIDIA Hackathon
  europepmc 2026 置信度 0.80
• Privacy-Preserving Federated Learning for Cross-Dataset EEG-Based Detection of Parkinson's Disease
  europepmc 2026 置信度 0.80
• Federated learning: A new frontier in the exploration of multi-institutional medical imaging data.
  Artificial intelligence has transformed the perspective of medical imaging, leading to a genuine technological revolution in modern computer-assisted healthcare systems. However, ubiquitously featured deep learning (DL) systems require access to a considerable…
  pubmed Ciupek D, Malawski M, Pieciak T 2026 置信度 0.82
• Ten Quick Tips for Biomedical Federated Learning
  europepmc 2026 置信度 0.80
• FedDPI-SH: A Quality and Similarity Aware Federated Learning Framework for Medical Image Analysis.
  Federated learning (FL) enables decentralized medical image analysis while preserving data privacy. However, conventional methods overlook client data heterogeneity and inter-client feature similarity, resulting in suboptimal performance. In this paper, our pr…
  pubmed P GL, A G, George AB, Tummala VMR 等 2026 置信度 0.82
• GTAFL: Addressing Test-Agnostic Long-Tailed Federated Learning in Medical Image.
  In healthcare, protecting patient privacy is crucial due to the sensitivity of medical data and its extensive accessibility. Federated Learning (FL) offers a decentralized and privacy-preserving training paradigm, making it an ideal solution for healthcare app…
  pubmed Chen G, Zeng J, Jiang H 2026 置信度 0.82
• DDFL: dual defense against poisoning attacks in privacy-preserving federated learning.
  Federated learning (FL) offers a solution to data silos by enabling collaborative training of a global model across decentralized environments. However, when operating with semi-honest servers or malicious clients, traditional FL faces critical privacy and sec…
  pubmed Guo C, Tian M, Li X, Sun H 等 2026 置信度 0.82
• Incomplete Multimodal Federated Learning via Masking and Contrasting Prototypes.
  europepmc 2026 置信度 0.80
• JS-Drift: A reproducible Jensen-Shannon divergence procedure for drift-aware client weighting in federated learning.
  Federated learning lets institutions train a shared model without exchanging raw data, but standard aggregation (FedAvg) assumes client data distributions are stationary. In practice they drift over time, and aggregation that ignores this lets unstable clients…
  pubmed A S, Arumugam S, A E 2026 置信度 0.82
• PureChain web-based energy predictor with federated learning Dirichlet for real-time energy consumption forecasting.
  Accurate energy consumption forecasting in smart grids requires privacy-preserving learning mechanisms that remain effective under heterogeneous data distributions and support real-time operation. Existing federated learning approaches remain limited by poor p…
  pubmed Nanteza AL, Ahakonye LAC, Kim DS, Lee JM 2026 置信度 0.82
• Editorial Note: A scalable blockchain-enabled federated learning architecture for edge computing.
  pubmed PLOS One Editors 2026 置信度 0.82
• Secure Federated Learning Framework with Dynamic Lightweight Cipher (Fed- DLC) for Resource-Constrained Networks and Data-Sensitive Applications
  europepmc 2026 置信度 0.80
• MerkleFL: A Secure Decentralized Federated Learning Framework for Healthcare with Model Integrity Verification
  Abstract Decentralized federated learning (DFL) offers a privacy-preserving approach for collaboratively training models across distributed healthcare entities without sharing raw patient data. However, ensuring the integrity and reliability of model updates i…
  europepmc Ashwin Verma, Sunil Pathak, Pronaya Bhattacharya 2026 置信度 0.80
• Federated Learning in Anesthesiology: A Privacy-Preserving Approach to Collaborative Predictive Modeling.
  pubmed Almeida VFA, Dantas M, Donato G, Aggarwal A 2026 置信度 0.82
• A blockchain-assisted secure federated learning architecture for intrusion detection in internet of things networks.
  The fast growth of Internet of Things (IoT) systems has made them very susceptible to advanced cyber-attacks, and an intelligent and privacy-sustainable intrusion detection system is required. Conventional centralized intrusion detection frameworks have the dr…
  pubmed Kamran M, Akhtar SM, Gilani A, Alhashmi AA 等 2026 置信度 0.82
• FedMIR: Multimodal Federated Learning with Missing Modality Imputation and Distribution-Aware Routing.
  Existing multimodal federated learning methods typically assume complete modality availability and struggle with heterogeneity between training and testing data distributions, making them unsuitable for handling missing modalities and distribution drift in dis…
  pubmed Xiong H, Dai M 2026 置信度 0.82
• FireSmoke-FL: a privacy-preserving federated learning framework for real-time fire and smoke detection.
  The early detection of fire and smoke is a significant aspect in the prevention of disasters in smart cities and the development of extensive monitoring systems. In such scenarios, the early response is critical in ensuring the safety and prevention of further…
  pubmed Alblehai F, Alalwan N, Alzahrani AI, Al-Bayatti AH 等 2026 置信度 0.82
• Federated Learning Based on Fuzzy Fusion Rules for Chemical Production Process Fault Diagnosis.
  Process data plays a vital role in diagnosing fault sources in chemical production. However, such data contain rich process information and are often sensitive, making direct analysis infeasible due to privacy concerns. Although federated learning mitigates da…
  pubmed Xu Y, Yang W, Du S, Zhang M 2026 置信度 0.82
• Quantum federated learning for autonomous vehicle cybersecurity: An analytical review of architectures and threat landscapes.
  With the rapid rollout of autonomous vehicles (AVs), many AV criminal attacks form one of the biggest cybersecurity attack surfaces in contemporary infrastructure. But there are also the AV subsystems-LiDAR, radar, cameras, GPS, CAN buses, and V2X communicatio…
  pubmed T S, Devadas RM 2026 置信度 0.82
• Design of Privacy-Preserving Federated Learning Models for Auto Insurance Telematics
  The integration of telematics in the automobile insurance industry has facilitated the transition toward Usage-Based Insurance (UBI). However, the centralized collection of granular GPS and accelerometer data poses significant privacy risks to policyholders. T…
  europepmc John Davis, Jennifer Smith, Robert Williams 2026 置信度 0.80
• TeleZK-FL: enabling trustless and verifiable remote patient monitoring via quantized zero-knowledge federated learning.
  The rapid expansion of Internet of Medical Things (IoMT) and telehealth platforms has generated vast amounts of patient data suitable for training diagnostic Artificial Intelligence (AI) models. However, strict privacy regulations (HIPAA, GDPR) and the risk of…
  pubmed Jayaraman P, Delhibabu R 2026 置信度 0.82
• Experimentally Validated Quantum-Secure Federated Learning over a Multi-user Quantum Network.
  Federated learning enables decentralized, privacy-preserving training but remains vulnerable to privacy leakage in the quantum era. Quantum federated learning (QFL) offers a promising path toward enhanced security and efficiency. However, a practical and exper…
  pubmed Liu ZP, Cao XY, Liu HW, Sun XR 等 2026 置信度 0.82
• Securing IoMT with Federated Learning: A Hybrid Deep Learning and Ensemble-Based Intrusion Detection Framework
  Abstract The rapid proliferation of Internet of Medical Things (IoMT) devices has significantly enhanced healthcare connectivity while simultaneously increasing exposure to sophisticated cyber threats such as distributed denial-of-service attacks, malware prop…
  europepmc Ifeanyi Nwokoro, Edgar Osaghae, Saheed Kayode, Tombari Sibe 2026 置信度 0.80
• Improving IoT security through an explainable hybrid CNN-transformer model and federated learning.
  The rapid proliferation of Internet of Things (IoT) devices has intensified cybersecurity threats, exposing critical infrastructure to sophisticated intrusion attacks. Existing intrusion detection systems (IDS) typically rely on centralized architectures that …
  pubmed Al-Hejri AM, Al-Tam RM, Sable AH, Alshamrani SS 等 2026 置信度 0.82
• Explainable AI Approaches in Federated Learning: Systematic Review.
  Background Artificial intelligence (AI) has, in the recent past, experienced a rebirth with the growth of generative AI systems such as ChatGPT and Bard. These systems are trained with billions of parameters and have enabled widespread accessibility and unders…
  pubmed Titus Tunduny, Bernard Shibwabo, Tunduny T, Shibwabo B 2025-10-11T14:55:07Z 置信度 0.82
• Adaptive Multi-Model Hierarchical Federated Learning for Robust IoT Intrusion Detection.
  The rapid growth of the Internet of Things (IoT) has introduced significant cybersecurity challenges in highly distributed, heterogeneous, and privacy-sensitive environments. Traditional centralized intrusion detection approaches and conventional federated lea…
  pubmed Latif S, Djenouri D 2026 置信度 0.82
• BlockFedX: a cross-domain federated learning system with explainability, anomaly detection, and tamper-evident logging.
  Many organisations collect sensitive data that cannot be freely shared. Hospitals store brain magnetic resonance imaging (MRI) scans on internal servers; banks keep transaction records behind strict firewalls; agricultural services retain crop images in isolat…
  pubmed Naidu KS, Suma GJ 2026 置信度 0.82
• SFedCA: Credit Assignment-Based Active Client Selection Strategy for Spiking Federated Learning.
  europepmc 2026 置信度 0.80
• Developing Predictive Models by Sharing Predictions - An Investigation of a Federated Learning Approach for ADMET Predictions.
  Machine learning models for ADMET prediction benefit from large, diverse data sets, yet such data are typically siloed across organizations. Federated learning (FL) enables collaborative modeling while preserving data privacy. Here, we investigate a student-te…
  pubmed Guha R, Wang W, Price E, Bhhatarai B 等 2026 置信度 0.82
• Federated Learning over Serverless Edge Clusters: Balancing Privacy, Latency, and Trust
  Abstract The proliferation of Internet of Things (IoT) devices has driven the need for decentralized machine learning paradigms that preserve data privacy while minimizing transmission latency. Federated Learning (FL) has emerged as a robust solution, allowing…
  europepmc Poonam Verma 2026 置信度 0.80
• Privacy-preserving verification of preprocessing in federated learning for genomic data.
  europepmc 2026 置信度 0.80
• Enabling Privacy-Preserving Federated Learning in Healthcare: The FLAME Architecture and Policy Framework.
  Federated Learning enables collaborative AI development in healthcare without sharing patient data, addressing privacy and regulatory constraints like GDPR and HIPAA. We present FLAME, an open-source platform developed within the German PrivateAIM project, des…
  pubmed de Arruda Botelho Herr M, Placzek P, Abu Attieh H, Schultz B 等 2026 置信度 0.82