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• Federated Learning is Still Machine Learning! Epistemic vs. Ethical Issues Common Across Medical ML Models.
  pubmed Neal JP, Morar N 2026 置信度 0.82
• Energy-Adaptive Multi-Dimensional Learning Control for Federated Learning in Energy-Harvesting AIoT Systems.
  This paper addresses the problem of efficient federated learning in energy-harvesting AIoT systems, where time-varying energy availability may lead to device blackouts and unstable learning performance. To address this issue, we propose an energy-adaptive mult…
  pubmed Noh DK, Kwak C 2026 置信度 0.82
• A privacy-preserving federated learning framework for generalizable CBCT to synthetic CT translation in head and neck.
  Cone-beam computed tomography (CBCT) has become a widely adopted modality for image-guided radiotherapy (IGRT). However, CBCT is characterized by increased noise, limited soft-tissue contrast, and artifacts. These issues result in unreliable Hounsfield unit (H…
  pubmed Raggio CB, Zaffino P, Spadea MF 2026 置信度 0.82
• Improving Cross-Center Generalization for Multi-modal MRI Meningioma Segmentation via Glioma-Pretrained Federated Learning.
  Automated meningioma segmentation on multi-modal MRI remains challenging when models are transferred across institutions, because scanner protocols, image characteristics, and annotation styles may differ between centers. Federated learning (FL) provides a pri…
  pubmed Ni C, Qian K, Zhang C, Zhang R 等 2026 置信度 0.82
• A Federated Learning Framework for Mental Stress Assessment and Brain Source Localization Using EEG and fNIRS
  Abstract Mental stress influences how individuals think, perform tasks, and maintain long-term well-being, which makes its assessment a nontrivial problem in both clinical practice and everyday work environments. Although machine learning methods have been ado…
  europepmc Muhammad Yaqub, Degang Xu 2026 置信度 0.80
• Federated Learning Under Evolving Distribution Shifts.
  europepmc 2026 置信度 0.80
• Federated learning frameworks: quality and interoperability for biomedical research.
  pubmed Chavero-Diez M, Hernandez-Ferrer C, Codó L, Gelpí JL 等 2026 置信度 0.82
• A cloud-edge-end collaborative intelligent caching method based on incremental federated learning algorithms.
  In a cloud-edge-end collaborative system, data generated by terminal devices often contains users' sensitive information and is constantly generated and changing, leading to potential data privacy leaks in caches. Additionally, due to the inability to promptly…
  pubmed Huang X, Jin L, Lin K, Wu W 等 2026 置信度 0.82
• Federated Learning IoT Security Framework Based on Dynamic Multi Domain Mapping and Robust Aggregation
  Abstract The paper proposes a federated learning IoT security framework based on dynamic multi domain mapping (DME) and robust adversarial gain aggregation (R-AGA) to address the performance and security challenges posed by non independent identically distribu…
  europepmc Yi Tian 2026 置信度 0.80
• Bias-Corrected Federated Learning for Video Recommendation over Stochastic Communication Links.
  With the increasing demand for privacy-preserving and real-time personalized services in large-scale video platforms, designing robust federated recommendation frameworks over practical communication networks has become increasingly important. To this end, thi…
  pubmed Zhou C, Pei Y, Li Z 2026 置信度 0.82
• FedDAAW: Dynamic Client Selection and Accuracy- Adaptive Aggregation for Cross-Task Federated Learning under Heterogeneity
  Abstract Federated learning enables collaborative model training across distributed clients while preserving data privacy. However, both statistical and system heterogeneity among clients severely degrade model performance. Existing methods mostly address only…
  europepmc Jianqing Tang 2026 置信度 0.80
• Contrastive Federated Learning for Graph Anomaly Detection.
  europepmc 2026 置信度 0.80
• Robust and Lightweight Federated Learning for NB-IoT Security: A Blockchain-Verified CNN-RNN Approach.
  The rapid proliferation of Narrowband Internet of Things (NB-IoT) devices necessitates robust, privacy-preserving intrusion detection systems. While Federated Learning (FL) mitigates data privacy risks through localized training, it introduces vulnerabilities …
  pubmed Özmen G, Yiltas-Kaplan D 2026 置信度 0.82
• BC-AWFedAvg: blockchain-assisted adaptive federated learning for secure RAN Slicing in beyond-5G networks.
  The evolution of beyond-5G networks introduces new challenges for radio resource management, particularly for heterogeneous service requirements across multiple virtual network operators. This work presents BC-AWFedAvg, a layered framework for federated deep r…
  pubmed Zemzemi M, Hajlaoui JE, Aldalbahi AS, Mhatli S 2026 置信度 0.82
• BFLAFD: blockchain-enabled federated learning framework for adaptive fire detection in IIoT networks.
  The Industrial Internet of Things (IIoT) is a network of interconnected sensors, devices, and control systems in the oil and gas sectors that has been developed to make the industries automated and continuously monitored. However, there are challenges in fire …
  pubmed Desikan J, Singh SK, Jayanthiladevi A, Gupta H 2026 置信度 0.82
• Asynchronous federated learning with partial weights aggregation for energy consumption forecasting.
  Accurate energy forecasting is essential for grid stability, demand-side management, and efficient renewable integration. However, energy consumption data collected from smart meters may expose sensitive user information, thus raising privacy concerns. Federat…
  pubmed Toderean L, Mesesan M, Cioara T, Anghel I 2026 置信度 0.82
• MediVault: An Auditable and Secure Federated Learning System for Privacy-Preserving Healthcare Collaboration
  europepmc 2026 置信度 0.80
• Decentralised Federated Learning Framework for Privacy-Preserving Medical Insurance and Sustainable Rural Economic Development in Tamil Nadu
  europepmc 2026 置信度 0.80
• Use of Federated Learning for validating and updating privacy-preserving decentralized multi-study prognostic models in Traumatic Brain Injury
  Developing modern clinical prediction models (CPMs) and advanced analytics requires large datasets, often necessitating data from different studies. Privacy regulations may hinder data sharing, especially across countries. Decentralized federated data infrastr…
  pubmed Torres-Espin A, Wong JC, Hinson HE, Kuipers TB 等 2026 置信度 0.82
• Trust-Aware and Energy-Efficient Federated Learning for Secure Sensor Networks at the Edge.
  The widespread adoption of large-scale sensor networks in privacy-sensitive and safety-critical applications has intensified the demand for secure, trustworthy, and energy-efficient learning mechanisms at the network edge. Federated learning has emerged as a p…
  pubmed Reis MJCS 2026 置信度 0.82
• Context-Aware Online Model Splitting and Device Association for Semi-Decentralized Federated Learning in Internet of Things.
  As a distributed approach to Artificial Intelligence (AI) model construction over wireless networks, federated learning (FL) based on multi-device collaborative training can protect data privacy, as well as increase the computing load of local model updates. I…
  pubmed Xu B, Wang S, Tang X 2026 置信度 0.82
• Analysis of federated learning on non-independent and identically distributed sleep data.
  europepmc 2026 置信度 0.80
• PRIVACY AMPLIFIER: Benchmarking Poisoning Membership Inference for Federated Learning
  europepmc 2026 置信度 0.80
• Governance-Aware Federated Learning for Trustworthy and Compliant Decentralized Infrastructure Monitoring
  Abstract The paper introduces a federated learning (GFL) architecture that is governed to monitor decentralized infrastructure in a legally heterogeneous setting. The framework incorporates legal compliance limits, auditability controls, and policy alignment a…
  europepmc Seyed Amirhossein Mousaviniya Qasemabadi, Mohammadmahdi Rezaeifar Sangchouli, Fatemeh Zahra HosseiniMoghadam Shadman 2026 置信度 0.80
• A crowdsensing intrusion detection dataset for decentralized federated learning models.
  europepmc 2026 置信度 0.80
• DAG-CTFL: DAG Blockchain Cross-Layer Authentication Framework for Trustworthy IoV Federated Learning.
  Privacy-preserving federated learning in the internet of vehicles (IoV) requires low-latency authentication, bounded privacy leakage, and robustness against malicious model updates. However, most existing studies separately design communication authentication …
  pubmed Liao L, Chen L 2026 置信度 0.82
• Exploring the Vulnerabilities of Federated Learning: A Deep Dive Into Gradient Inversion Attacks.
  pubmed Guo P, Wang R, Zeng S, Zhu J 等 2026 置信度 0.82
• Correction: Securing Federated Learning With Blockchain in the Medical Field: Systematic Literature Review.
  pubmed Wang X, Xie Y, Chen X, Yang J 等 2026 置信度 0.82
• FedLiverNet: a federated learning framework for privacy-preserving and efficient liver cancer detection.
  Liver cancer continues to be a significant health issue on the global front, and proper segmentation of the liver and the tumor formed from the computed tomography is essential in the early diagnosis and subsequent treatment strategies. Although deep learning …
  pubmed Lou L, Govindarajan V, Shaikh ZA, Liu R 等 2026 置信度 0.82
• Dataset of round-level federated learning and layer-2 blockchain overhead from ten raspberry Pi edge clients.
  This data article describes round-level records from 40 federated learning sessions run on a physical testbed of ten Raspberry Pi 4 Model B devices acting as edge clients, with an Ubuntu workstation as the aggregation server. Each session trains a one-dimensio…
  pubmed Bayan T, Mukhambetiyar B, Boranbayev A, Yazici A 2026 置信度 0.82
• Federated learning for privacy-preserving skin cancer classification using deep neural networks.
  Skin cancer is a significant health issue in the entire world and there is a need to have diagnostic systems that are precise, enlargeable and privacy safeguarding. The heterogeneity of the institution and the issue of patient confidentiality frequently restri…
  pubmed Alfalahi MAM, Karan O, Kurnaz S, Türkben AK 2026 置信度 0.82
• IMFLKD: an incentive mechanism for decentralized federated learning based on knowledge distillation.
  europepmc 2026 置信度 0.80
• FedDAD: Federated Recommendation with Dual Additive Decoupling
  Official implementation of FedDAD for federated learning research. This repository contains the source code to reproduce the experimental results in the paper Federated Recommendation with Dual Additive Decoupling.
  datacite Shi, Liuhao, Ni, Zhengwei 2026 置信度 0.66
• FedDAD: Federated Recommendation with Dual Additive Decoupling
  Official implementation of FedDAD for federated learning research. This repository contains the source code to reproduce the experimental results in the paper Federated Recommendation with Dual Additive Decoupling.
  datacite Shi, Liuhao, Ni, Zhengwei 2026 置信度 0.66
• Decentralized Learning with Federated Bayesian Networks
  This paper proposes a novel decentralized learning algorithm for Bayesian networks, termed Federated Bayesian Networks (FBNs). The core idea is to enable nodes within a network to learn independently and collaboratively, mirroring the principles of federated l…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Decentralized Learning with Federated Bayesian Networks
  This paper proposes a novel decentralized learning algorithm for Bayesian networks, termed Federated Bayesian Networks (FBNs). The core idea is to enable nodes within a network to learn independently and collaboratively, mirroring the principles of federated l…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Federated Cognitive Networks
  This record contains the complete reproducibility package for the paper "Federated Cognitive Networks: Trust-Weighted Personalized Federated Learning for Heterogeneous Cross-Silo Clinical Data" (Anas Shahin, Bahaa Masry; 2026). Federated Cognitive Networks (FC…
  datacite Shahin, Anas, Masry, Bahaa 2026 置信度 0.66
• Federated Cognitive Networks
  This record contains the complete reproducibility package for the paper "Federated Cognitive Networks: Trust-Weighted Personalized Federated Learning for Heterogeneous Cross-Silo Clinical Data" (Anas Shahin, Bahaa Masry; 2026). Federated Cognitive Networks (FC…
  datacite Shahin, Anas, Masry, Bahaa 2026 置信度 0.66
• Privacy-Preserving Techniques for Secure Cloud Computing : A Survey of Recent Advances
  Cloud computing has gained immense popularity in recent years due to its on-demand and scalable computing resources. However, with the growth of cloud computing, privacy and security concerns have also increased. The primary concern is how to ensure the confid…
  datacite Savitha, N., Kiran, Dr. E. Sai 2023 置信度 0.66 Privacy-preserving techniquesSecure cloud computingfederated learninghomomorphic encryptionsecure multi-party computation
• Privacy-Preserving Techniques for Secure Cloud Computing : A Survey of Recent Advances
  Cloud computing has gained immense popularity in recent years due to its on-demand and scalable computing resources. However, with the growth of cloud computing, privacy and security concerns have also increased. The primary concern is how to ensure the confid…
  datacite Savitha, N., Kiran, Dr. E. Sai 2023 置信度 0.66 Privacy-preserving techniquesSecure cloud computingfederated learninghomomorphic encryptionsecure multi-party computation
• A Conceptual Model for Intelligent Automation Loops in High-Throughput, Multi-Phase Crude Processing Units
  The increasing complexity and scale of high-throughput, multi-phase crude processing units (CUs) in modern refineries necessitate the development of advanced automation strategies beyond conventional control loops. Traditional systems, typically based on fixed…
  datacite Ofoedu, Andrew Tochukwu, Ozor, Joshua Emeka, Sofoluwe, Oludayo, Jambol, Dazok Donald 2023 置信度 0.66 Conceptual ModelIntelligent AutomationLoopsHigh-ThroughputMulti-Phase
• A Conceptual Model for Intelligent Automation Loops in High-Throughput, Multi-Phase Crude Processing Units
  The increasing complexity and scale of high-throughput, multi-phase crude processing units (CUs) in modern refineries necessitate the development of advanced automation strategies beyond conventional control loops. Traditional systems, typically based on fixed…
  datacite Ofoedu, Andrew Tochukwu, Ozor, Joshua Emeka, Sofoluwe, Oludayo, Jambol, Dazok Donald 2023 置信度 0.66 Conceptual ModelIntelligent AutomationLoopsHigh-ThroughputMulti-Phase
• Enhanced Brain Tumor Detection and Privacy Preserving Using Federated Learning
  Brain cancers pose significant difficulties for both diagnosis and treatment, underscoring the necessity for precise and private-protecting detection techniques. Federated learning is used to solve this, allowing several healthcare facilities to work together …
  datacite Nandan, Uday, Sai, Chetan, Sai, Naga, Viswanadapalli, Anusha 2024 置信度 0.66 CNN; Deep learning; Brain Tumor; Densenet121; Federated Learning
• Enhanced Brain Tumor Detection and Privacy Preserving Using Federated Learning
  Brain cancers pose significant difficulties for both diagnosis and treatment, underscoring the necessity for precise and private-protecting detection techniques. Federated learning is used to solve this, allowing several healthcare facilities to work together …
  datacite Nandan, Uday, Sai, Chetan, Sai, Naga, Viswanadapalli, Anusha 2024 置信度 0.66 CNN; Deep learning; Brain Tumor; Densenet121; Federated Learning
• Enhancing Database Architectures with Artificial Intelligence (AI)
  Artificial intelligence and Database Management Systems Integration bring intelligence, adaptability, and independence in the world of databases. Relational database management systems structure the data and have been the foundations for implementing them, alt…
  datacite Maddali, Gopikrishna 2025 置信度 0.66 Artificial Intelligence; Database Management System; AI-DBMS Integration; NoSQL; NewSQL; Intelligent Databases; Query Optimization
• Enhancing Database Architectures with Artificial Intelligence (AI)
  Artificial intelligence and Database Management Systems Integration bring intelligence, adaptability, and independence in the world of databases. Relational database management systems structure the data and have been the foundations for implementing them, alt…
  datacite Maddali, Gopikrishna 2025 置信度 0.66 Artificial Intelligence; Database Management System; AI-DBMS Integration; NoSQL; NewSQL; Intelligent Databases; Query Optimization
• Distributed Knowledge Graph Embedding with Federated Learning for Privacy Preservation
  Knowledge graph embedding techniques have gained significant traction in representing complex relationships within knowledge graphs, enabling applications such as link prediction, entity recommendation, and semantic search. However, the training of these embed…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Distributed Knowledge Graph Embedding with Federated Learning for Privacy Preservation
  Knowledge graph embedding techniques have gained significant traction in representing complex relationships within knowledge graphs, enabling applications such as link prediction, entity recommendation, and semantic search. However, the training of these embed…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Distributed Bayesian Inference with Federated Learning and Differential Privacy
  This paper presents a novel approach to distributed Bayesian inference that leverages the strengths of federated learning and differential privacy. The core idea is to execute Bayesian inference locally on a network of devices, aggregating updates while simult…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Distributed Bayesian Inference with Federated Learning and Differential Privacy
  This paper presents a novel approach to distributed Bayesian inference that leverages the strengths of federated learning and differential privacy. The core idea is to execute Bayesian inference locally on a network of devices, aggregating updates while simult…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Secure Parameter Aggregation and Distributed Anomaly Detection in Cross-Cloud Data Center Networks
  This study proposes an intelligent method based on federated learning to address the problem of anomaly detection in distributed cross-cloud data center environments. The research first analyzes the complexity and risks in multi-tenant shared computing scenari…
  datacite Zhang, Shirui 2024 置信度 0.66
• Secure Parameter Aggregation and Distributed Anomaly Detection in Cross-Cloud Data Center Networks
  This study proposes an intelligent method based on federated learning to address the problem of anomaly detection in distributed cross-cloud data center environments. The research first analyzes the complexity and risks in multi-tenant shared computing scenari…
  datacite Zhang, Shirui 2024 置信度 0.66
• Decentralized Federated Learning with Differential Privacy for Scientific Data
  This paper presents a novel approach to collaborative scientific data analysis leveraging Decentralized Federated Learning with Differential Privacy (DFLDP). The core challenge in many scientific domains is the reluctance to share raw data due to stringent pri…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Decentralized Federated Learning with Differential Privacy for Scientific Data
  This paper presents a novel approach to collaborative scientific data analysis leveraging Decentralized Federated Learning with Differential Privacy (DFLDP). The core challenge in many scientific domains is the reluctance to share raw data due to stringent pri…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Decentralized Federated Learning with Byzantine Fault Tolerance via Graph-Based Consensus
  Federated learning (FL) has emerged as a promising paradigm for training machine learning models on decentralized data sources without directly exchanging the data itself. However, this approach is susceptible to Byzantine attacks, where malicious nodes intent…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Decentralized Federated Learning with Byzantine Fault Tolerance via Graph-Based Consensus
  Federated learning (FL) has emerged as a promising paradigm for training machine learning models on decentralized data sources without directly exchanging the data itself. However, this approach is susceptible to Byzantine attacks, where malicious nodes intent…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Information-Theoretic Framework for Trustworthy Federated Learning
  Federated Learning (FL) offers a promising paradigm for decentralized machine learning, enabling collaborative model training without direct data sharing. However, traditional privacy-preserving techniques within FL often rely on ad-hoc assumptions and lack a …
  datacite Zhang, Jincheng 2026 置信度 0.66
• Information-Theoretic Framework for Trustworthy Federated Learning
  Federated Learning (FL) offers a promising paradigm for decentralized machine learning, enabling collaborative model training without direct data sharing. However, traditional privacy-preserving techniques within FL often rely on ad-hoc assumptions and lack a …
  datacite Zhang, Jincheng 2026 置信度 0.66
• Radiomics and Machine Learning for Predicting Immunotherapy Response in Solid Tumors
  Immunotherapy, particularly immune checkpoint inhibitors (ICIs), has transformed the treatment landscape for numerous solid tumors, including non-small cell lung cancer (NSCLC), melanoma, renal cell carcinoma, hepatocellular carcinoma, and urothelial carcinoma…
  datacite Aditya Verma*1, Shatrughna Nagrik2, Priya Krishnan3, Rohit Chatterjee4 2026 置信度 0.66 Radiomics; Machine learning; Immunotherapy; Immune checkpoint inhibitors; Precision oncology; Radiogenomics; Artificial intelligence; Solid tumors.
• Radiomics and Machine Learning for Predicting Immunotherapy Response in Solid Tumors
  Immunotherapy, particularly immune checkpoint inhibitors (ICIs), has transformed the treatment landscape for numerous solid tumors, including non-small cell lung cancer (NSCLC), melanoma, renal cell carcinoma, hepatocellular carcinoma, and urothelial carcinoma…
  datacite Aditya Verma*1, Shatrughna Nagrik2, Priya Krishnan3, Rohit Chatterjee4 2026 置信度 0.66 Radiomics; Machine learning; Immunotherapy; Immune checkpoint inhibitors; Precision oncology; Radiogenomics; Artificial intelligence; Solid tumors.
• A Novel Framework for Trustworthy Privacy Preserving Machine Learning Model for Industrial IoT Systems Using Blockchain Techniques
  Industrial Internet of Things (IIoT) is changing many driving enterprises like transportation, mining, horticulture, energy and medical care. Machine Learning calculations are utilized for getting stages for IT frameworks. The IoT network unit hubs typically a…
  datacite Yedukondalu, Dr. G., Rao, Dr. Channapragada Rama Seshagiri, Dugyala, Raman 2022 置信度 0.66 IIoT trustworthinessblockchainsEthereumfederated learningdifferential privacy
• A Novel Framework for Trustworthy Privacy Preserving Machine Learning Model for Industrial IoT Systems Using Blockchain Techniques
  Industrial Internet of Things (IIoT) is changing many driving enterprises like transportation, mining, horticulture, energy and medical care. Machine Learning calculations are utilized for getting stages for IT frameworks. The IoT network unit hubs typically a…
  datacite Yedukondalu, Dr. G., Rao, Dr. Channapragada Rama Seshagiri, Dugyala, Raman 2022 置信度 0.66 IIoT trustworthinessblockchainsEthereumfederated learningdifferential privacy
• Privacy-Preserving Data Processing in Cloud : From Homomorphic Encryption to Federated Analytics
  Privacy-preserving data processing refers to the methods and models that allow computing and analyzing sensitive data with a guarantee of confidentiality. As cloud computing and applications that rely on data continue to expand, there is an increasing need to …
  datacite Sarraf, Gaurav, Pal, Vibhor 2022 置信度 0.66 Privacy-PreservingHomomorphic EncryptionSecure Multi-Party ComputationDifferential PrivacyFederated Analytics
• Privacy-Preserving Data Processing in Cloud : From Homomorphic Encryption to Federated Analytics
  Privacy-preserving data processing refers to the methods and models that allow computing and analyzing sensitive data with a guarantee of confidentiality. As cloud computing and applications that rely on data continue to expand, there is an increasing need to …
  datacite Sarraf, Gaurav, Pal, Vibhor 2022 置信度 0.66 Privacy-PreservingHomomorphic EncryptionSecure Multi-Party ComputationDifferential PrivacyFederated Analytics
• Blockchain-Based Federated Learning with Differential Privacy
  Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without direct data sharing. However, traditional FL systems remain vulnerable to privacy breaches and data manipulation. This paper proposes …
  datacite Zhang, Jincheng 2026 置信度 0.66
• Blockchain-Based Federated Learning with Differential Privacy
  Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without direct data sharing. However, traditional FL systems remain vulnerable to privacy breaches and data manipulation. This paper proposes …
  datacite Zhang, Jincheng 2026 置信度 0.66
• SpecGradFilter: A Spectral Gradient Filtering Framework for Taming Federated Heterogeneity
  Federated Learning (FL) is fundamentally challenged by statistical heterogeneity, where non-identically distributed (non-IID) data induces client drift that severely hampers global convergence. While existing approaches attempt to mitigate this drift through s…
  datacite Yuan, Liyang, Yang, Yibo, Guo, Dandan, Richtarik, Peter 等 2026 置信度 0.66 Machine Learning (cs.LG)FOS: Computer and information sciences
• A Wolf in Sheep's Clothing: Targeted Routing Hijacking in Federated RAG
  Federated Retrieval-Augmented Generation (FedRAG) is attractive for privacy-sensitive applications because full local corpora remain on clients. As a result, routing must rely on client-provided semantic profiles, creating a new opportunity for manipulation. W…
  datacite Mu, Junjie, Li, Qiongxiu 2026 置信度 0.66 Cryptography and Security (cs.CR)Computation and Language (cs.CL)Information Retrieval (cs.IR)FOS: Computer and information sciences
• Ampere: Communication-Efficient and High-Accuracy Split Federated Learning
  A Federated Learning (FL) system collaboratively trains neural networks across devices and a server but is limited by significant on-device computation costs. Split Federated Learning (SFL) systems mitigate this by offloading a block of layers of the network f…
  datacite Zhang, Zihan, Wong, Leon, Varghese, Blesson 2025 置信度 0.66 Distributed, Parallel, and Cluster Computing (cs.DC)Machine Learning (cs.LG)FOS: Computer and information sciences
• Quantum Federated Learning Based on Bures--Uhlmann Geometry for Heterogeneous Noisy Clients
  Quantum federated learning enables collaborative model training across quantum devices without sharing raw data, and it faces the data and hardware heterogeneity inherent to noisy quantum devices. Utilizing the quantum geometric tensor is a natural remedy, yet…
  datacite Emori, Haruki, Uchihara, Masaki, Tokunaga, Yuuki 2026 置信度 0.66 Quantum Physics (quant-ph)Machine Learning (cs.LG)FOS: Physical sciencesFOS: Computer and information sciences
• FedEHR-Agents: Federated Agentic Optimization for Automated EHR Modeling
  Recent advances in large language models are enabling autonomous clinical agents to perform increasingly complex electronic health record (EHR) modeling workflows. However, agents deployed at individual hospitals remain constrained by institution-specific data…
  datacite Bai, Jun, Wang, Ruilin, Li, Yue 2026 置信度 0.66 Machine Learning (cs.LG)Artificial Intelligence (cs.AI)Multiagent Systems (cs.MA)FOS: Computer and information sciences
• FISGuard: Defending Against Membership Inference via Fixed Input Subspaces
  As large language models are increasingly adopted in federated learning, protecting user privacy while performing parameter-efficient fine-tuning on distributed private data has become an important challenge. Although clients only share gradients instead of di…
  datacite Jiang, Haocheng, Shen, Hua 2026 置信度 0.66 Cryptography and Security (cs.CR)Artificial Intelligence (cs.AI)Distributed, Parallel, and Cluster Computing (cs.DC)FOS: Computer and information sciences
• Initialization Is Critical: Advancing Federated Short-Term Load Forecasting under Load Heterogeneity via Model Initialization
  Short-term load forecasting (STLF) provides essential information for numerous applications in modern power systems. However, accurate STLF often relies on fine-grained smart-meter data from distributed users, raising increasing concerns about data privacy. Fe…
  datacite Chen, Jianing, Farhadi, Vajiheh, Li, Yan, La Porta, Thomas 2026 置信度 0.66 Machine Learning (cs.LG)Systems and Control (eess.SY)FOS: Computer and information sciencesFOS: Electrical engineering, electronic engineering, information engineering
• Revisiting Continuous Noise Sampling for Multi-Party Differential Privacy
  Combining secure multi-party computation (MPC) with differential privacy (DP) enables multiple parties to release aggregate statistics without a trusted curator, and the core primitive is the protocol to sample noise from a continuous distribution under finite…
  datacite Fu, Yucheng, Wang, Tianhao 2026 置信度 0.66 Cryptography and Security (cs.CR)FOS: Computer and information sciences
• Beyond Non-IID: Learner--Client Distribution Mismatch in Federated Learning
  Federated learning systems are increasingly deployed to facilitate collaborative model training across a heterogeneous client population. Existing practice mostly implicitly assumes that the aggregated client data distribution is representative of the learner'…
  datacite Xie, Yiming, Su, Lili, Mi, Ningfang 2026 置信度 0.66 Machine Learning (cs.LG)FOS: Computer and information sciences
• DART-FL: Burst-Aware Multitask Federated Learning under Dynamic Inference Demand at the Edge
  Edge intelligence systems increasingly require model training and online inference to coexist on resource-constrained devices, while inference demand can vary substantially across tasks over time. This creates two coupled challenges: sufficient computation mus…
  datacite Xie, Yiming, Yu, Pinrui, Yuan, Geng, Lin, Xue 等 2026 置信度 0.66 Machine Learning (cs.LG)FOS: Computer and information sciences
• Companion artifact: Spectrum Sensing from Classical Detection to Federated Learning — A Taxonomy, Benchmarking-Gap Analysis, and Deployment Guide
  Companion artifact for the survey "Spectrum Sensing from Classical Detection to Federated Learning: A Taxonomy, Benchmarking-Gap Analysis, and Deployment Guide". Contains three components: (1) a reference implementation of four classical spectrum-sensing detec…
  datacite Mohd Ali, Yazan Adnan, Kaymih, Nour Ahmad, Bany Salameh, Haythem A. 2026 置信度 0.66
• Companion artifact: Spectrum Sensing from Classical Detection to Federated Learning — A Taxonomy, Benchmarking-Gap Analysis, and Deployment Guide
  Companion artifact for the survey "Spectrum Sensing from Classical Detection to Federated Learning: A Taxonomy, Benchmarking-Gap Analysis, and Deployment Guide". Contains three components: (1) a reference implementation of four classical spectrum-sensing detec…
  datacite Mohd Ali, Yazan Adnan, Kaymih, Nour Ahmad, Bany Salameh, Haythem A. 2026 置信度 0.66
• AI-Driven EHR Architectures for Safer, Smarter Clinical Handoff Systems
  Clinical handoff failures remain a leading source of preventable adverse events in acute healthcare, with communication breakdown at care transitions implicated in approximately 80% of serious sentinel events. Despite widespread adoption of structured protocol…
  datacite Krishna Mattam 2026 置信度 0.66
• AI-Driven EHR Architectures for Safer, Smarter Clinical Handoff Systems
  Clinical handoff failures remain a leading source of preventable adverse events in acute healthcare, with communication breakdown at care transitions implicated in approximately 80% of serious sentinel events. Despite widespread adoption of structured protocol…
  datacite Krishna Mattam 2026 置信度 0.66
• The Decentralized Classroom: A Narrative Review of Federated Learning from Google's Keyboards to the Privacy's Frontier
  Federated learning---the artificial intelligence whose subject is the decentralized classroom and whose lesson is the model's travel---moved from Dwork's 2006 differential privacy and Shokri and Shmatikov's 2015 gradients through Konečný's 2016 compression, Mc…
  datacite Revista, Zen, IA, 10 2026 置信度 0.66 federated learningFedAvgdifferential privacysecure aggregationnon-IID data
• The Decentralized Classroom: A Narrative Review of Federated Learning from Google's Keyboards to the Privacy's Frontier
  Federated learning---the artificial intelligence whose subject is the decentralized classroom and whose lesson is the model's travel---moved from Dwork's 2006 differential privacy and Shokri and Shmatikov's 2015 gradients through Konečný's 2016 compression, Mc…
  datacite Revista, Zen, IA, 10 2026 置信度 0.66 federated learningFedAvgdifferential privacysecure aggregationnon-IID data
• SYNTHEMA Newsletter 7
  At the European Big Data Value Forum 2024, a dedicated HealthData4EU Cluster session highlighted the collaborative efforts of the Horizon-funded projects AISYM4MED, SYNTHEMA, and SECURED in advancing healthcare through artificial intelligence and privacy-prese…
  datacite AUSTRALO INTERINNOV MARKETING LAB SL 2024 置信度 0.66
• SYNTHEMA Newsletter 7
  At the European Big Data Value Forum 2024, a dedicated HealthData4EU Cluster session highlighted the collaborative efforts of the Horizon-funded projects AISYM4MED, SYNTHEMA, and SECURED in advancing healthcare through artificial intelligence and privacy-prese…
  datacite AUSTRALO INTERINNOV MARKETING LAB SL 2024 置信度 0.66
• Federated Learning Aggregation Techniques and Multimodal Model Efficiency at the Edge
  This report synthesises findings from 13 peer-reviewed papers addressing the following research question: What is the impact of different federated learning aggregation techniques on the inference efficiency and latency of multimodal models deployed across het…
  datacite Assignee Research 2026 置信度 0.66 impactdifferentfederatedlearningaggregation
• Federated Learning Aggregation Techniques and Multimodal Model Efficiency at the Edge
  This report synthesises findings from 13 peer-reviewed papers addressing the following research question: What is the impact of different federated learning aggregation techniques on the inference efficiency and latency of multimodal models deployed across het…
  datacite Assignee Research 2026 置信度 0.66 impactdifferentfederatedlearningaggregation
• Federated Learning-Based Intelligent Product Lifecycle Management Framework for Automotive Supply Chains: Enhancing Data Privacy, Predictive Analytics and Collaborative Decision-Making
  The automotive industry is experiencing a major transformation driven by digital technologies, distributed manufacturing systems and strict data governance requirements. Traditional Product Lifecycle Management (PLM) systems face limitations due to centralized…
  datacite Paladi Gopikrishna 2026 置信度 0.66 federated learning; product lifecycle management; automotive supply chain; differential privacy; Industry 5.0; predictive analytics; digital twin; non-IID data
• Federated Learning-Based Intelligent Product Lifecycle Management Framework for Automotive Supply Chains: Enhancing Data Privacy, Predictive Analytics and Collaborative Decision-Making
  The automotive industry is experiencing a major transformation driven by digital technologies, distributed manufacturing systems and strict data governance requirements. Traditional Product Lifecycle Management (PLM) systems face limitations due to centralized…
  datacite Paladi Gopikrishna 2026 置信度 0.66 federated learning; product lifecycle management; automotive supply chain; differential privacy; Industry 5.0; predictive analytics; digital twin; non-IID data
• The Economic and Civilizational Valuation of Cryptographically Tethered Audio Portfolios within the CollectiveOS Architecture
  The Economic and Civilizational Valuation of Cryptographically Tethered Audio Portfolios within the CollectiveOS Architecture The valuation of digital audio assets has historically been bound to the mechanics of the "Extractive Age," a paradigm that relies on …
  datacite Brewer, Mark Anthony 2026 置信度 0.66
• The Economic and Civilizational Valuation of Cryptographically Tethered Audio Portfolios within the CollectiveOS Architecture
  The Economic and Civilizational Valuation of Cryptographically Tethered Audio Portfolios within the CollectiveOS Architecture The valuation of digital audio assets has historically been bound to the mechanics of the "Extractive Age," a paradigm that relies on …
  datacite Brewer, Mark Anthony 2026 置信度 0.66
• SYNTHEMA Newsletter 8
  In its end-of-year newsletter, the SYNTHEMA consortium celebrated its ongoing research milestones and highlighted the groundbreaking contributions of its partner, Humanitas Research Hospital. Led by Professor Matteo Giovanni Della Porta, the Humanitas team mad…
  datacite AUSTRALO INTERINNOV MARKETING LAB SL 2024 置信度 0.66
• SYNTHEMA Newsletter 8
  In its end-of-year newsletter, the SYNTHEMA consortium celebrated its ongoing research milestones and highlighted the groundbreaking contributions of its partner, Humanitas Research Hospital. Led by Professor Matteo Giovanni Della Porta, the Humanitas team mad…
  datacite AUSTRALO INTERINNOV MARKETING LAB SL 2024 置信度 0.66
• Privacy Preserving Federated Or Post-Quantum Authentication Scheme
  The interplay between the advancements in quantum computing techniques and the adoption of the distributed learning approach pose an enormous challenge to conventional cryptographic authentication protocols. Traditional public key systems and federated learnin…
  datacite Farzeen Basith, A R Deepti 2026 置信度 0.66
• Privacy Preserving Federated Or Post-Quantum Authentication Scheme
  The interplay between the advancements in quantum computing techniques and the adoption of the distributed learning approach pose an enormous challenge to conventional cryptographic authentication protocols. Traditional public key systems and federated learnin…
  datacite Farzeen Basith, A R Deepti 2026 置信度 0.66
• Defense Against Information Integrity Attacks in Federated IoT Systems Using Inertial Momentum-Aware IALM-RPCA
  While federated learning offers a decentralized approach to model training, ensuring the integrity of the information from each IoT client remains a challenge. This work delves into the dynamics of multi-stage federated learning, its susceptibility to informat…
  datacite Oudarja Barman Tanmoy, Sakib Hasan, Adnan Anwar, Md Al Mamun 等 2026 置信度 0.66
• FL-CheX: A Federated Learning Benchmark for Chest X-ray Classification with Multi-Source Heterogeneity
  FL-CheX is a federated learning benchmark dataset designed for chest X-ray disease classification under realistic multi-source heterogeneity. The dataset simulates three types of real-world distribution shifts: 1. Demographic heterogeneity (age and gender imba…
  datacite Ullah, Md.Sajjad 2026 置信度 0.66
• FL-CheX: A Federated Learning Benchmark for Chest X-ray Classification with Multi-Source Heterogeneity
  FL-CheX is a federated learning benchmark dataset designed for chest X-ray disease classification under realistic multi-source heterogeneity. The dataset simulates three types of real-world distribution shifts: 1. Demographic heterogeneity (age and gender imba…
  datacite Ullah, Md.Sajjad 2026 置信度 0.66
• AdaptFedAvg: Privacy-Preserving Topic Difficulty Modeling in Heterogeneous Smart Classroom Networks
  AdaptFedAvg is a privacy-preserving federated learning framework for topic difficulty modeling in heterogeneous smart classroom networks. Educational institutions generate large volumes of student interaction data, but privacy regulations such as GDPR, FERPA, …
  datacite Korra, Kiran, Ch., Sarayu, Bonthu, Akshay, Thaduri, Shiva Nagesh 2026 置信度 0.66 Federated LearningMachine LearningPrivacySmart Classrooms
• AdaptFedAvg: Privacy-Preserving Topic Difficulty Modeling in Heterogeneous Smart Classroom Networks
  AdaptFedAvg is a privacy-preserving federated learning framework for topic difficulty modeling in heterogeneous smart classroom networks. Educational institutions generate large volumes of student interaction data, but privacy regulations such as GDPR, FERPA, …
  datacite Korra, Kiran, Ch., Sarayu, Bonthu, Akshay, Thaduri, Shiva Nagesh 2026 置信度 0.66 Federated LearningMachine LearningPrivacySmart Classrooms
• AMLNet: A Decentralised Anti-Money Laundering Detection Framework Using Federated Learning, Blockchain, and Zero-Knowledge Proofs
  One of these financial crimes, which seem to sound like a concept straight out of a dream until you get a sense of the magnitude of the issue, is money laundering. According to the United Nations, Between $800 billion and $2 trillion in illicit money is transa…
  datacite Priya S, Dakshayini M, Apsana S A, Anjana M R 2026 置信度 0.66