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• Connecting platforms via LTI: The university course "OER in HE" and the connection of Metacampus with iMooX.at (Report 02/2024)
  Schön, Sandra; Ebner, Martin & Gasplmayr, Katharina (2024). Connecting platforms via LTI: The university course “OER in HE” and the connection of Metacampus with iMooX.at. In: “Unite! Digital Teaching and Learning - Success Story Report” (edited by Martin Ebne…
  datacite Schön, Sandra, Ebner, Martin, Gasplmayr, Katharina 2024 置信度 0.66
• Connecting platforms via LTI: The university course "OER in HE" and the connection of Metacampus with iMooX.at (Report 02/2024)
  Schön, Sandra; Ebner, Martin & Gasplmayr, Katharina (2024). Connecting platforms via LTI: The university course “OER in HE” and the connection of Metacampus with iMooX.at. In: “Unite! Digital Teaching and Learning - Success Story Report” (edited by Martin Ebne…
  datacite Schön, Sandra, Ebner, Martin, Gasplmayr, Katharina 2024 置信度 0.66
• Showing knowledge with an open badge – Experiences from the "How to use Metacampus" course (Report 1/2024)
  Gasplmayr, Katharina & Schön, Sandra (2024). Showing knowledge with an open badge – Experiences from the “How to use Metacampus” course. In: Unite! Digital Teaching and Learning - Success Story Report (edited by Martin Ebner, Katharina Gasplmayr and Sandra Sch…
  datacite Gasplmayr, Katharina, Schön, Sandra 2024 置信度 0.66
• Showing knowledge with an open badge – Experiences from the "How to use Metacampus" course (Report 1/2024)
  Gasplmayr, Katharina & Schön, Sandra (2024). Showing knowledge with an open badge – Experiences from the “How to use Metacampus” course. In: Unite! Digital Teaching and Learning - Success Story Report (edited by Martin Ebner, Katharina Gasplmayr and Sandra Sch…
  datacite Gasplmayr, Katharina, Schön, Sandra 2024 置信度 0.66
• Federated virtual learning management in a European University alliance: General challenges and first experiences using LTI to connect LMS in Unite!
  Published: Schön, S., Ebner, M., Edelsbrunner, S., Gasplmayr, K., Hohla-Sejkora, K., Leitner, P. & Taraghi, B. (2024). Federated virtual learning management in a European University alliance: General challenges and first experiences using LTI to connect LMS in…
  datacite Schön, Sandra, Ebner, Martin, Edelsbrunner, Sarah, Gasplmayr, Katharina 等 2024 置信度 0.66
• Federated virtual learning management in a European University alliance: General challenges and first experiences using LTI to connect LMS in Unite! (Preprint)
  Published as: Schön, S., Ebner, M., Edelsbrunner, S., Gasplmayr, K., Hohla-Sejkora, K., Leitner, P. & Taraghi, B. (2024). Federated virtual learning management in a European University alliance: General challenges and first experiences using LTI to connect LMS…
  datacite Schön, Sandra, Ebner, Martin, Edelsbrunner, Sarah, Gasplmayr, Katharina 等 2024 置信度 0.66
• Federated virtual learning management in a European University alliance: General challenges and first experiences using LTI to connect LMS in Unite! (Preprint)
  Published as: Schön, S., Ebner, M., Edelsbrunner, S., Gasplmayr, K., Hohla-Sejkora, K., Leitner, P. & Taraghi, B. (2024). Federated virtual learning management in a European University alliance: General challenges and first experiences using LTI to connect LMS…
  datacite Schön, Sandra, Ebner, Martin, Edelsbrunner, Sarah, Gasplmayr, Katharina 等 2024 置信度 0.66
• Communication-Efficient Federated Learning With Data and Client Heterogeneity
  Federated Learning (FL) enables large-scale distributed training of machine learning models, while still allowing individual nodes to maintain data locally. However, executing FL at scale comes with inherent practical challenges: 1) heterogeneity of the local …
  datacite Zakerinia, Hossein, Talaei, Shayan, Nadiradze, Giorgi, Alistarh, Dan 2022 置信度 0.66 Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• PROSurvival clinical data conversion scripts
  This archive contains a Python script that was used to prepare the clinical data collected in the PROSurvival project ("Survival Prediction for Prostate Cancer Patients using Federated Machine Learning and Predictive Morphological Patterns") from two universit…
  datacite Xu, Tingyan 2025 置信度 0.66 PROSurvivalProstate cancer
• PROSurvival clinical data conversion scripts
  This archive contains a Python script that was used to prepare the clinical data collected in the PROSurvival project ("Survival Prediction for Prostate Cancer Patients using Federated Machine Learning and Predictive Morphological Patterns") from two universit…
  datacite Xu, Tingyan 2025 置信度 0.66 PROSurvivalProstate cancer
• AgnostiqHQ/covalent: v0.232.0
  [0.232.0] - 2024-01-10 Authors Ara Ghukasyan 38226926+araghukas@users.noreply.github.com Co-authored-by: pre-commit-ci[bot] Andrew S. Rosen asrosen93@gmail.com Co-authored-by: Will Cunningham wjcunningham7@users.noreply.github.com Co-authored-by: Sankalp Sanan…
  datacite Will Cunningham, Alejandro Esquivel, Casey Jao, Sankalp Sanand 等 2024 置信度 0.66
• Joint Model Pruning and Resource Allocation for Wireless Time-triggered Federated Learning
  Time-triggered federated learning, in contrast to conventional event-based federated learning, organizes users into tiers based on fixed time intervals. However, this network still faces challenges due to a growing number of devices and limited wireless bandwi…
  datacite Zhang, Xinlu, Deng, Yansha, Mahmoodi, Toktam 2024 置信度 0.66 Machine Learning (cs.LG)Information Theory (cs.IT)FOS: Computer and information sciencesFOS: Computer and information sciences
• Workflow Optimization for Parallel Split Learning
  Split learning (SL) has been recently proposed as a way to enable resource-constrained devices to train multi-parameter neural networks (NNs) and participate in federated learning (FL). In a nutshell, SL splits the NN model into parts, and allows clients (devi…
  datacite Tirana, Joana, Tsigkari, Dimitra, Iosifidis, George, Chatzopoulos, Dimitris 2024 置信度 0.66 Distributed, Parallel, and Cluster Computing (cs.DC)Machine Learning (cs.LG)Networking and Internet Architecture (cs.NI)FOS: Computer and information sciencesFOS: Computer and information sciences
• Integrating Predictive Analytics into Diabetic Wound Care: A Path Toward Personalized Medicine. A Narrative Review
  ABSTRACT Diabetic foot ulcers (DFUs) are a severe and costly complication of diabetes, often leading to infections, amputations, and diminished quality of life. Traditional wound care remains reactive, relying on subjective assessments and lacking predictive c…
  datacite Lufulwabo, Aime 2025 置信度 0.66
• Integrating Predictive Analytics into Diabetic Wound Care: A Path Toward Personalized Medicine. A Narrative Review
  ABSTRACT Diabetic foot ulcers (DFUs) are a severe and costly complication of diabetes, often leading to infections, amputations, and diminished quality of life. Traditional wound care remains reactive, relying on subjective assessments and lacking predictive c…
  datacite Lufulwabo, Aime 2025 置信度 0.66
• Flooding Attack SD-IoD
  The data provided was used during the flooding attack detection experiment using semi-supervised federated learning in Software Defined Internet of Drones. The flooding attack is performed on three distinct IoD zones where the attacker drones exist within each…
  datacite Sumadi, Fauzi, Alsubhi, Khalid 2025 置信度 0.66 Denial-of-Service AttackSoftware Defined NetworkCyber Attack
• Privacy and Ethical Concerns in AI-Powered Data Processing
  Artificial intelligence is increasingly being used to process large datasets. This introduces serious privacy and security risks (Paul, 2024). In many AI systems, sensitive personal data are collected and analyzed, so leaks or attacks can expose private inform…
  datacite B. Ziade, Tony 2025 置信度 0.66 PrivacyGenetic Privacy/ethicsEthicsEthicsEthics
• Privacy and Ethical Concerns in AI-Powered Data Processing
  Artificial intelligence is increasingly being used to process large datasets. This introduces serious privacy and security risks (Paul, 2024). In many AI systems, sensitive personal data are collected and analyzed, so leaks or attacks can expose private inform…
  datacite B. Ziade, Tony 2025 置信度 0.66 PrivacyGenetic Privacy/ethicsEthicsEthicsEthics
• TurboSVM-FL: Boosting Federated Learning through SVM Aggregation for Lazy Clients
  Federated learning is a distributed collaborative machine learning paradigm that has gained strong momentum in recent years. In federated learning, a central server periodically coordinates models with clients and aggregates the models trained locally by clien…
  datacite Wang, Mengdi, Bodonhelyi, Anna, Bozkir, Efe, Kasneci, Enkelejda 2024 置信度 0.66 Machine Learning (cs.LG)Distributed, Parallel, and Cluster Computing (cs.DC)FOS: Computer and information sciencesFOS: Computer and information sciences
• Like Oil and Water: Group Robustness Methods and Poisoning Defenses May Be at Odds
  Group robustness has become a major concern in machine learning (ML) as conventional training paradigms were found to produce high error on minority groups. Without explicit group annotations, proposed solutions rely on heuristics that aim to identify and then…
  datacite Panaitescu-Liess, Michael-Andrei, Kaya, Yigitcan, Zhu, Sicheng, Huang, Furong 等 2025 置信度 0.66 Machine Learning (cs.LG)Cryptography and Security (cs.CR)FOS: Computer and information sciencesFOS: Computer and information sciences
• SBAC-PAD_2024_Paper_Reproducibility
  A summary for reproducibility of the paper "Optimal Time and Energy-Aware Client Selection Algorithms for Federated Learning on Heterogeneous Resources" (SBAC-PAD 2024).
  datacite Nunes, Alan Lira 2024 置信度 0.66 Physical Sciences and MathematicsComputer Sciences
• Unlearning during Learning: An Efficient Federated Machine Unlearning Method
  In recent years, Federated Learning (FL) has garnered significant attention as a distributed machine learning paradigm. To facilitate the implementation of the right to be forgotten, the concept of federated machine unlearning (FMU) has also emerged. However, …
  datacite Gu, Hanlin, Zhu, Gongxi, Zhang, Jie, Zhao, Xinyuan 等 2024 置信度 0.66 Machine Learning (cs.LG)Distributed, Parallel, and Cluster Computing (cs.DC)FOS: Computer and information sciencesFOS: Computer and information sciences
• Cooperative Hardware-Prompt Learning for Snapshot Compressive Imaging
  Existing reconstruction models in snapshot compressive imaging systems (SCI) are trained with a single well-calibrated hardware instance, making their performance vulnerable to hardware shifts and limited in adapting to multiple hardware configurations. To fac…
  datacite Wang, Jiamian, Wu, Zongliang, Zhang, Yulun, Yuan, Xin 等 2023 置信度 0.66 Computer Vision and Pattern Recognition (cs.CV)FOS: Computer and information sciencesFOS: Computer and information sciences
• A deep cut into Split Federated Self-supervised Learning
  Collaborative self-supervised learning has recently become feasible in highly distributed environments by dividing the network layers between client devices and a central server. However, state-of-the-art methods, such as MocoSFL, are optimized for network div…
  datacite Przewięźlikowski, Marcin, Osial, Marcin, Zieliński, Bartosz, Śmieja, Marek 2024 置信度 0.66 Machine Learning (cs.LG)Artificial Intelligence (cs.AI)Distributed, Parallel, and Cluster Computing (cs.DC)FOS: Computer and information sciencesFOS: Computer and information sciences
• Flooding Attack SD-IoD
  The data provided was used during the flooding attack detection experiment using semi-supervised federated learning in Software Defined Internet of Drones. The flooding attack is performed on three distinct IoD zones where the attacker drones exist within each…
  datacite Sumadi, Fauzi, Alsubhi, Khalid 2025 置信度 0.66 Denial-of-Service AttackSoftware Defined NetworkCyber Attack
• AI stridor analysis
  Objective: This review examines artificial intelligence (AI) applications in analyzing respiratory sounds, specifically stridor, to address limitations in traditional, operator-dependent diagnostic methods. Data Sources: A structured search across PubMed, Scop…
  datacite Tartaglia, Francesco Carlo, Motisi, Annagiulia 2025 置信度 0.66 Physical Sciences and MathematicsOtorhinolaryngologic DiseasesDiseasesMedicine and Health SciencesComputer Sciences
• Overcoming Data and Model Heterogeneities in Decentralized Federated Learning via Synthetic Anchors
  Conventional Federated Learning (FL) involves collaborative training of a global model while maintaining user data privacy. One of its branches, decentralized FL, is a serverless network that allows clients to own and optimize different local models separately…
  datacite Huang, Chun-Yin, Srinivas, Kartik, Zhang, Xin, Li, Xiaoxiao 2024 置信度 0.66 Machine Learning (cs.LG)Artificial Intelligence (cs.AI)FOS: Computer and information sciencesFOS: Computer and information sciences
• Building OER Competence Across the Unite! Alliance. Insights from the "Unite! OER Courses" Project. Presentation at Unite! Ed Future Conference, March 13, 2025
  The "Unite! OER Courses" seed fund project successfully advanced Open Educational Resources (OER) competence among students and lecturers across the Unite! alliance. A major milestone was the development of a Massive Open Online Course (MOOC) on OER, licensed …
  datacite Schön, Sandra, Ebner, Martin, Vicente-Saéz, Rubén 2025 置信度 0.66
• Building OER Competence Across the Unite! Alliance. Insights from the "Unite! OER Courses" Project. Presentation at Unite! Ed Future Conference, March 13, 2025
  The "Unite! OER Courses" seed fund project successfully advanced Open Educational Resources (OER) competence among students and lecturers across the Unite! alliance. A major milestone was the development of a Massive Open Online Course (MOOC) on OER, licensed …
  datacite Schön, Sandra, Ebner, Martin, Vicente-Saéz, Rubén 2025 置信度 0.66
• Controlled privacy leakage propagation throughout overlapping grouped learning
  Federated Learning (FL) is the standard protocol for collaborative learning. In FL, multiple workers jointly train a shared model. They exchange model updates calculated on their data, while keeping the raw data itself local. Since workers naturally form group…
  datacite Kiani, Shahrzad, Boenisch, Franziska, Draper, Stark C. 2025 置信度 0.66 Machine Learning (cs.LG)Cryptography and Security (cs.CR)Distributed, Parallel, and Cluster Computing (cs.DC)FOS: Computer and information sciencesFOS: Computer and information sciences
• A Lightweight and Secure Deep Learning Model for Privacy-Preserving Federated Learning in Intelligent Enterprises
  The ever growing Internet of Things (IoT) connections drive a new type of organization, the Intelligent Enterprise. In intelligent enterprises, machine learning based models are adopted to extract insights from data. Due to the efficiency and privacy challenge…
  datacite Fotohi, Reza, Aliee, Fereidoon Shams, Farahani, Bahar 2025 置信度 0.66 Cryptography and Security (cs.CR)Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• Federated Learning in Practice: Reflections and Projections
  Federated Learning (FL) is a machine learning technique that enables multiple entities to collaboratively learn a shared model without exchanging their local data. Over the past decade, FL systems have achieved substantial progress, scaling to millions of devi…
  datacite Daly, Katharine, Eichner, Hubert, Kairouz, Peter, McMahan, H. Brendan 等 2024 置信度 0.66 Machine Learning (cs.LG)Artificial Intelligence (cs.AI)Cryptography and Security (cs.CR)FOS: Computer and information sciencesFOS: Computer and information sciences
• Where is the Testbed for my Federated Learning Research?
  Progressing beyond centralized AI is of paramount importance, yet, distributed AI solutions, in particular various federated learning (FL) algorithms, are often not comprehensively assessed, which prevents the research community from identifying the most promi…
  datacite Božič, Janez, Faustino, Amândio R., Radovič, Boris, Canini, Marco 等 2024 置信度 0.66 Machine Learning (cs.LG)Distributed, Parallel, and Cluster Computing (cs.DC)FOS: Computer and information sciencesFOS: Computer and information sciences
• pFedLVM: A Large Vision Model (LVM)-Driven and Latent Feature-Based Personalized Federated Learning Framework in Autonomous Driving
  Deep learning-based Autonomous Driving (AD) models often exhibit poor generalization due to data heterogeneity in an ever domain-shifting environment. While Federated Learning (FL) could improve the generalization of an AD model (known as FedAD system), conven…
  datacite Kou, Wei-Bin, Lin, Qingfeng, Tang, Ming, Xu, Sheng 等 2024 置信度 0.66 Robotics (cs.RO)Distributed, Parallel, and Cluster Computing (cs.DC)FOS: Computer and information sciencesFOS: Computer and information sciences
• Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning
  We present the first study on provably efficient randomized exploration in cooperative multi-agent reinforcement learning (MARL). We propose a unified algorithm framework for randomized exploration in parallel Markov Decision Processes (MDPs), and two Thompson…
  datacite Hsu, Hao-Lun, Wang, Weixin, Pajic, Miroslav, Xu, Pan 2024 置信度 0.66 Machine Learning (cs.LG)Machine Learning (stat.ML)FOS: Computer and information sciencesFOS: Computer and information sciences
• A Primal-Dual Algorithm for Hybrid Federated Learning
  Very few methods for hybrid federated learning, where clients only hold subsets of both features and samples, exist. Yet, this scenario is extremely important in practical settings. We provide a fast, robust algorithm for hybrid federated learning that hinges …
  datacite Overman, Tom, Blum, Garrett, Klabjan, Diego 2022 置信度 0.66 Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• FACT or Fiction: Can Truthful Mechanisms Eliminate Federated Free Riding?
  Standard federated learning (FL) approaches are vulnerable to the free-rider dilemma: participating agents can contribute little to nothing yet receive a well-trained aggregated model. While prior mechanisms attempt to solve the free-rider dilemma, none have a…
  datacite Bornstein, Marco, Bedi, Amrit Singh, Mohamed, Abdirisak, Huang, Furong 2024 置信度 0.66 Computer Science and Game Theory (cs.GT)Distributed, Parallel, and Cluster Computing (cs.DC)Machine Learning (cs.LG)Theoretical Economics (econ.TH)FOS: Computer and information sciences
• Accurate Forgetting for Heterogeneous Federated Continual Learning
  Recent years have witnessed a burgeoning interest in federated learning (FL). However, the contexts in which clients engage in sequential learning remain under-explored. Bridging FL and continual learning (CL) gives rise to a challenging practical problem: fed…
  datacite Wuerkaixi, Abudukelimu, Cui, Sen, Zhang, Jingfeng, Yan, Kunda 等 2025 置信度 0.66 Machine Learning (cs.LG)Artificial Intelligence (cs.AI)FOS: Computer and information sciencesFOS: Computer and information sciences
• An Information Theoretic Perspective on Conformal Prediction
  Conformal Prediction (CP) is a distribution-free uncertainty estimation framework that constructs prediction sets guaranteed to contain the true answer with a user-specified probability. Intuitively, the size of the prediction set encodes a general notion of u…
  datacite Correia, Alvaro H. C., Massoli, Fabio Valerio, Louizos, Christos, Behboodi, Arash 2024 置信度 0.66 Machine Learning (cs.LG)Information Theory (cs.IT)Machine Learning (stat.ML)FOS: Computer and information sciencesFOS: Computer and information sciences
• Contrastive Federated Learning with Tabular Data Silos
  Learning from vertical partitioned data silos is challenging due to the segmented nature of data, sample misalignment, and strict privacy concerns. Federated learning has been proposed as a solution. However, sample misalignment across silos often hinders opti…
  datacite Ginanjar, Achmad, Li, Xue, Hua, Wen, Pei, Jiaming 2024 置信度 0.66 Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciencesI.1.168A00
• Noise-Aware Algorithm for Heterogeneous Differentially Private Federated Learning
  High utility and rigorous data privacy are of the main goals of a federated learning (FL) system, which learns a model from the data distributed among some clients. The latter has been tried to achieve by using differential privacy in FL (DPFL). There is often…
  datacite Malekmohammadi, Saber, Yu, Yaoliang, Cao, Yang 2024 置信度 0.66 Machine Learning (cs.LG)Cryptography and Security (cs.CR)Distributed, Parallel, and Cluster Computing (cs.DC)FOS: Computer and information sciencesFOS: Computer and information sciences
• Federated Learning over Connected Modes
  Statistical heterogeneity in federated learning poses two major challenges: slow global training due to conflicting gradient signals, and the need of personalization for local distributions. In this work, we tackle both challenges by leveraging recent advances…
  datacite Grinwald, Dennis, Wiesner, Philipp, Nakajima, Shinichi 2024 置信度 0.66 Machine Learning (cs.LG)Distributed, Parallel, and Cluster Computing (cs.DC)FOS: Computer and information sciencesFOS: Computer and information sciences
• Federated Self-supervised Domain Generalization for Label-efficient Polyp Segmentation
  Employing self-supervised learning (SSL) methodologies assumes par-amount significance in handling unlabeled polyp datasets when building deep learning-based automatic polyp segmentation models. However, the intricate privacy dynamics surrounding medical data …
  datacite Tan, Xinyi, Wang, Jiacheng, Wang, Liansheng 2025 置信度 0.66 Computer Vision and Pattern Recognition (cs.CV)Distributed, Parallel, and Cluster Computing (cs.DC)Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• Dual Defense: Enhancing Privacy and Mitigating Poisoning Attacks in Federated Learning
  Federated learning (FL) is inherently susceptible to privacy breaches and poisoning attacks. To tackle these challenges, researchers have separately devised secure aggregation mechanisms to protect data privacy and robust aggregation methods that withstand poi…
  datacite Xu, Runhua, Gao, Shiqi, Li, Chao, Joshi, James 等 2025 置信度 0.66 Cryptography and Security (cs.CR)Artificial Intelligence (cs.AI)FOS: Computer and information sciences
• DualFed: Enjoying both Generalization and Personalization in Federated Learning via Hierachical Representations
  In personalized federated learning (PFL), it is widely recognized that achieving both high model generalization and effective personalization poses a significant challenge due to their conflicting nature. As a result, existing PFL methods can only manage a tra…
  datacite Zhu, Guogang, Liu, Xuefeng, Niu, Jianwei, Tang, Shaojie 等 2024 置信度 0.66 Machine Learning (cs.LG)Distributed, Parallel, and Cluster Computing (cs.DC)FOS: Computer and information sciencesFOS: Computer and information sciences
• From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcare
  Federated learning holds great potential for enabling large-scale healthcare research and collaboration across multiple centres while ensuring data privacy and security are not compromised. Although numerous recent studies suggest or utilize federated learning…
  datacite Li, Ming, Xu, Pengcheng, Hu, Junjie, Tang, Zeyu 等 2024 置信度 0.66 Machine Learning (cs.LG)Artificial Intelligence (cs.AI)FOS: Computer and information sciencesFOS: Computer and information sciences
• Enhancing Quantum Security over Federated Learning via Post-Quantum Cryptography
  Federated learning (FL) has become one of the standard approaches for deploying machine learning models on edge devices, where private training data are distributed across clients, and a shared model is learned by aggregating locally computed updates from each…
  datacite Li, Pingzhi, Chen, Tianlong, Liu, Junyu 2024 置信度 0.66 Quantum Physics (quant-ph)Artificial Intelligence (cs.AI)Cryptography and Security (cs.CR)Machine Learning (cs.LG)FOS: Physical sciences
• Hiding in Plain Sight: Disguising Data Stealing Attacks in Federated Learning
  Malicious server (MS) attacks have enabled the scaling of data stealing in federated learning to large batch sizes and secure aggregation, settings previously considered private. However, many concerns regarding the client-side detectability of MS attacks were…
  datacite Garov, Kostadin, Dimitrov, Dimitar I., Jovanović, Nikola, Vechev, Martin 2024 置信度 0.66 Cryptography and Security (cs.CR)Machine Learning (cs.LG)FOS: Computer and information sciencesI.2.11Privacy
• Distributed Intrusion Detection in Dynamic Networks of UAVs using Few-Shot Federated Learning
  Flying Ad Hoc Networks (FANETs), which primarily interconnect Unmanned Aerial Vehicles (UAVs), present distinctive security challenges due to their distributed and dynamic characteristics, necessitating tailored security solutions. Intrusion detection in FANET…
  datacite Ceviz, Ozlem, Sen, Sevil, Sadioglu, Pinar 2025 置信度 0.66 Cryptography and Security (cs.CR)FOS: Computer and information sciencesFOS: Computer and information sciences
• Communication-Efficient Personalized Federated Learning for Speech-to-Text Tasks
  To protect privacy and meet legal regulations, federated learning (FL) has gained significant attention for training speech-to-text (S2T) systems, including automatic speech recognition (ASR) and speech translation (ST). However, the commonly used FL approach …
  datacite Du, Yichao, Zhang, Zhirui, Yue, Linan, Huang, Xu 等 2024 置信度 0.66 Computation and Language (cs.CL)Sound (cs.SD)Audio and Speech Processing (eess.AS)FOS: Computer and information sciencesFOS: Computer and information sciences
• Tutorial: Privacy 101, with Trumpets and Truffles - An Introduction to Differential Privacy and Homomorphic Encryption, and Applications in Federated Learning
  IEEE WIFS 2024, On-site workshop, 2-5 December 2024, Italy
  datacite Fernando Pérez-González 2024 置信度 0.66
• Poisoning Attacks on Federated Learning-based Wireless Traffic Prediction
  Federated Learning (FL) offers a distributed framework to train a global control model across multiple base stations without compromising the privacy of their local network data. This makes it ideal for applications like wireless traffic prediction (WTP), whic…
  datacite Zhang, Zifan, Fang, Minghong, Huang, Jiayuan, Liu, Yuchen 2024 置信度 0.66 Networking and Internet Architecture (cs.NI)Cryptography and Security (cs.CR)Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• Convergence Analysis of Split Federated Learning on Heterogeneous Data
  Split federated learning (SFL) is a recent distributed approach for collaborative model training among multiple clients. In SFL, a global model is typically split into two parts, where clients train one part in a parallel federated manner, and a main server tr…
  datacite Han, Pengchao, Huang, Chao, Tian, Geng, Tang, Ming 等 2024 置信度 0.66 Distributed, Parallel, and Cluster Computing (cs.DC)Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• Ferrari: Federated Feature Unlearning via Optimizing Feature Sensitivity
  The advent of Federated Learning (FL) highlights the practical necessity for the right to be forgotten for all clients, allowing them to request data deletion from the machine learning models service provider. This necessity has spurred a growing demand for Fe…
  datacite Gu, Hanlin, Ong, Win Kent, Chan, Chee Seng, Fan, Lixin 2024 置信度 0.66 Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• Gradient Compression and Correlation Driven Federated Learning for Wireless Traffic Prediction
  Wireless traffic prediction plays an indispensable role in cellular networks to achieve proactive adaptation for communication systems. Along this line, Federated Learning (FL)-based wireless traffic prediction at the edge attracts enormous attention because o…
  datacite Zhang, Chuanting, Zhang, Haixia, Dang, Shuping, Shihada, Basem 等 2025 置信度 0.66 Distributed, Parallel, and Cluster Computing (cs.DC)FOS: Computer and information sciencesFOS: Computer and information sciences
• Election of Collaborators via Reinforcement Learning for Federated Brain Tumor Segmentation
  Federated learning (FL) enables collaborative model training across decentralized datasets while preserving data privacy. However, optimally selecting participating collaborators in dynamic FL environments remains challenging. We present RL-HSimAgg, a novel re…
  datacite Khan, Muhammad Irfan, Kontio, Elina, Khan, Suleiman A., Jafaritadi, Mojtaba 2024 置信度 0.66 Machine Learning (cs.LG)Computer Vision and Pattern Recognition (cs.CV)FOS: Computer and information sciencesFOS: Computer and information sciences
• Recommender Engine Driven Client Selection in Federated Brain Tumor Segmentation
  This study presents a robust and efficient client selection protocol designed to optimize the Federated Learning (FL) process for the Federated Tumor Segmentation Challenge (FeTS 2024). In the evolving landscape of FL, the judicious selection of collaborators …
  datacite Khan, Muhammad Irfan, Kontio, Elina, Khan, Suleiman A., Jafaritadi, Mojtaba 2024 置信度 0.66 Machine Learning (cs.LG)Computer Vision and Pattern Recognition (cs.CV)FOS: Computer and information sciencesFOS: Computer and information sciences
• Federated Learning in The Age of Foundation Models
  SPS Webinar, 10 December 2024, Dr. Ziyue Xu
  datacite Ziyue Xu 2024 置信度 0.66
• Federated Learning in The Age of Foundation Models
  SPS Webinar, 10 December 2024, Dr. Ziyue Xu
  datacite Ziyue Xu 2024 置信度 0.66
• Exploring Parameter-Efficient Fine-Tuning to Enable Foundation Models in Federated Learning
  Federated learning (FL) has emerged as a promising paradigm for enabling the collaborative training of models without centralized access to the raw data on local devices. In the typical FL paradigm (e.g., FedAvg), model weights are sent to and from the server …
  datacite Sun, Guangyu, Khalid, Umar, Mendieta, Matias, Wang, Pu 等 2022 置信度 0.66 Machine Learning (cs.LG)Computer Vision and Pattern Recognition (cs.CV)FOS: Computer and information sciencesFOS: Computer and information sciences
• Safely Learning with Private Data: A Federated Learning Framework for Large Language Model
  Private data, being larger and quality-higher than public data, can greatly improve large language models (LLM). However, due to privacy concerns, this data is often dispersed in multiple silos, making its secure utilization for LLM training a challenge. Feder…
  datacite Zheng, JiaYing, Zhang, HaiNan, Wang, LingXiang, Qiu, WangJie 等 2024 置信度 0.66 Cryptography and Security (cs.CR)Computation and Language (cs.CL)FOS: Computer and information sciencesFOS: Computer and information sciences
• FRTP: Federating Route Search Records to Enhance Long-term Traffic Prediction
  Accurate traffic prediction, especially predicting traffic conditions several days in advance is essential for intelligent transportation systems (ITS). Such predictions enable mid- and long-term traffic optimization, which is crucial for efficient transportat…
  datacite Ge, Hangli, Yang, Xiaojie, Matsunaga, Itsuki, Huang, Dizhi 等 2024 置信度 0.66 Artificial Intelligence (cs.AI)FOS: Computer and information sciencesFOS: Computer and information sciences
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  crossref Adrian Edin, Zheng Chen 2024-08-15T17:18:59Z 置信度 0.70
• Optimisation with Deep Learning for Leukaemia Classification in Federated Learning
  crossref Smritilekha Das, PADMANABAN K 2024-06-02T13:00:13Z 置信度 0.70
• Beyond Data Sharing: Enhancing IoT Intrusion Detection with Blockchain-Enabled Federated Learning
  Federated Learning (FL) is a decentralized ML approach that can be used for intrusion detection in Internet of Things (IoT) devices. It involves the local training of AI models and their aggregation at a central server. This methodology eliminates the need for…
  crossref Aditya Durgadas Naik, Raj Mani Shukla 2024-12-10T15:35:46Z 置信度 0.70
• Federated Learning: Attacks, Defenses, Opportunities and Challenges
  crossref Ghazaleh Shirvani, Saeid Ghasemshirazi, Behzad Beigzadeh 2025-01-21T13:23:03Z 置信度 0.70
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  crossref Mickaël Bettinelli, Alexandre Benoit, Kévin Grandjean 2025-01-16T18:31:23Z 置信度 0.70
• CL-FedFR: Curriculum Learning for Federated Face Recognition
  crossref Devilliers Dube, Çiğdem Erdem, Ömer Korçak 2024-03-03T18:09:35Z 置信度 0.70
• Grasp Control Method for Robotic Manipulator Based on Federated Reinforcement Learning
  crossref Shida Zhong, Yue Wang, Tao Yuan 2024-01-08T22:22:55Z 置信度 0.70
• Abdominal Multi-Organ Segmentation Using Federated Learning
  crossref Govind Yadav, Annappa B, Sachin D N 2024-11-19T18:38:53Z 置信度 0.70
• Federated continual learning based on prototype learning
  crossref HaoDong ZHANG, Liu YANG, Jian YU, QingHua HU 等 2024-08-22T06:09:17Z 置信度 0.70
• Personalized Federated Learning for Modulation Classification in Communication Systems with Differential Privacy
  crossref Peggy Sue Mathis, Dinh Nguyen 2024-01-13T05:18:22Z 置信度 0.70
• Privacy-Enhanced Image Restoration in Remote Sensing via Federated Learning
  This research addresses the dual challenges of image restoration quality and data privacy in optical remote sensing. Traditional restoration methods often fall short due to the complex nature of remote sensing images, and data privacy concerns further complica…
  crossref Muhammad Jahanzeb Khan, Suman Rath, Muhammad Hassan Zaib 2024-04-08T13:32:25Z 置信度 0.70
• Federated Learning for Heterogeneous Bandits with Unobserved Contexts
  crossref Jiabin Lin, Shana Moothedath 2024-08-19T13:25:01Z 置信度 0.70
• Privacy-Preserving Backdoor Attacks Mitigation in Federated Learning Using Functional Encryption
  crossref Funminivi Olagunju, Isaac Adom, Mahmoud Nabil Mahmoud 2024-04-24T17:22:34Z 置信度 0.70
• Marvel: Towards Efficient Federated Learning on IoT Devices
  crossref Libin Liu, Xiuting Xu 2024-03-27T17:02:54Z 置信度 0.70
• Mutual Information-Based Feature Selection for Federated Learning Environments
  crossref Samuel Suárez-Marcote, Laura Morán-Fernández, Verónica Bolón-Canedo 2025-03-04T18:39:11Z 置信度 0.70
• MetaClusterFL: Personalized Federated Learning on Non-IID data with Meta-learning and Clustering
  crossref Hui Zeng, Shiyu Xiong, Hongzhou Shi 2024-09-09T17:35:05Z 置信度 0.70
• Balancing Privacy Preservation and Accuracyoptimization in Federated Learning for E-Health
  crossref Rihab Saidi, Tarek Moulahi, Salah Zidi 2024-09-05T23:20:26Z 置信度 0.70
• An Efficient and Secure Federated Learning Communication Framework
  crossref Hassan Noura, Khalil Hariss 2024-07-17T17:18:34Z 置信度 0.70
• Architectural Patterns for the Design of Federated Learning Systems
  crossref Vijay Anand Rajasekaran, Jayalakshmi Periyasamy, Madala Guru Brahmam, Balamurugan Baluswamy 2024-11-22T21:19:26Z 置信度 0.70
• Intelligent Digital Twin (DT) for Wireless Systems
  crossref Mohsen Guizani 2025-01-21T18:22:35Z 置信度 0.70
• A Federated Learning Algorithm Based on Combination of Prototype and Comparative Learning
  crossref Yanwei Chen, Wenbo Huang 2024-10-29T17:28:51Z 置信度 0.70
• Evaluating gradient inversion attacks and defenses
  crossref Yangsibo Huang, Samyak Gupta, Zhao Song, Sanjeev Arora 等 2024-02-23T09:49:06Z 置信度 0.70
• Federated Learning: Introduction, Evolution, Working, Advantages, and Its Application in Various Domains
  crossref Manoj Kumar Pandey*, Naresh Kumar Kar, Priyanka Gupta 2024-11-22T21:19:26Z 置信度 0.70
• Considerations on the theory of training models with differential privacy
  crossref Marten van Dijk, Phuong Ha Nguyen 2024-02-23T09:48:34Z 置信度 0.70
• In-Network Federated Learning Control
  crossref Nour El Houda Yellas, Bernardetta Addis, Selma Boumerdassi, Roberto Riggio 等 2024-03-05T16:15:04Z 置信度 0.70
• A Federated Learning Approach for Non-Co-Located Datasets: Enhancing Data Governance and Privacy
  crossref 2024-09-30T08:53:03Z 置信度 0.70
• Federated Learning for Privacy-Preserving AI: Revolutionizing Data Sharing Across Industries
  Federated Learning (FL) has emerged as a groundbreaking approach to Artificial Intelligence (AI) that preserves user privacy while enabling collaborative model training across diverse datasets. This survey highlights the evolution, architecture, methodologies,…
  crossref Sharathchandra Patil, Sai Geethanjali K, Nidhi Umashankar 2024-11-28T23:40:44Z 置信度 0.70
• Architecture and design choices for federated learning in modern digital healthcare systems
  crossref Konstantinos A. Koutsopoulos, Christoph Thümmler, Angelica Avila Castillo, Alice Abend 等 2024-06-07T07:07:42Z 置信度 0.70
• Enhancing Data Security in Federated Learning with Dilithium
  Federated learning (FL) enables multiple parties to collaboratively train machine learning models while preserving data privacy. However, securing communication within FL frameworks remains a significant challenge due to potential vulnerabilities to data breac…
  crossref Quoc Bao Phan, Hien Nguyen, Phap Duong Ngoc, Tuy Nguyen 2024-10-22T16:31:00Z 置信度 0.70
• Heterogeneity-Aware Device Selection for Efficient Federated Edge Learning
  crossref Yiran Shi, Jieyan Nie, Xingwei Li, Hui Li 2024-04-15T09:58:00Z 置信度 0.70
• Blockchain Empowered Federated Learning
  crossref Reza Nourmohammadi 2024-04-05T15:12:41Z 置信度 0.70
• Learned Model Compression for Efficient and Privacy-Preserving Federated Learning
  Federated learning (FL) performs collaborative training of deep learning models among multiple clients, safeguarding data privacy, security, and legal adherence by preserving training data locally. Despite the benefits of FL, its wider implementation is hinder…
  crossref Yiming Chen, Lusine Abrahamyan, Hichem Sahli, Nikos Deligiannis 2024-07-15T15:00:15Z 置信度 0.70
• NestFL: Enhancing federated learning through nested multi-capacity model pruning in heterogeneous edge computing
  crossref Xiaomao Zhou, Qingmin Jia, Yujiao Hu, Renchao Xie 2024-10-04T06:18:55Z 置信度 0.70
• Probabilistic Free Riding Attack in Federated Learning
  crossref Anee Sharma, Ningrinla Marchang 2024-11-11T18:37:45Z 置信度 0.70
• Application Domains of Federated Learning
  crossref S. Annamalai, N. Sangeetha, M. Kumaresan, Dommaraju Tejavarma 等 2024-11-22T21:19:26Z 置信度 0.70
• Vertical Federated Learning-Based Decentralized Voltage Prediction Considering Interarea Coupling and Privacy Preservation
  crossref Jianfeng Yan, Beibei Wang 2024-07-24T18:22:16Z 置信度 0.70
• (Dp)2fl:Dynamic Personalized Differential Privacy Federated Learning
  crossref Cuiyun Shi, jingyu wang, Lixin Liu, Xin Chang 等 2024-11-01T13:39:12Z 置信度 0.70
• HE-Floc: Federated Learning Fingerprint Indoor Localization Method Based on Homomorphic Encryption
  crossref Chengze Li, Xuejun Zhang 2025-04-14T17:35:47Z 置信度 0.70