Leveraging Gradient Noise for Detection and Filtering of Byzantine Clients by Latifa Errami, Vyacheslav Kungurtsev, El Houcine Bergou

“…Distributed Learning enables multiple clients to collaboratively train large models on private, decentralized data. …”
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Detection of Malicious Clients in Federated Learning Using Graph Neural Network by Anee Sharma, Ningrinla Marchang

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FedNDA: Enhancing Federated Learning with Noisy Client Detection and Robust Aggregation by Tuan Dung Kieu, Charles Fonbonne, Trung-Kien Tran, Thi-Lan Le, Hai Vu, Huu-Thanh Nguyen, Thanh-Hai Tran

“…To address this problem, this paper introduces a Federated learning framework with Noisy client Detection and robust Aggregation, FedNDA. In the first stage, FedNDA detects noisy clients by analyzing the distribution of their local losses. …”
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Federated Learning for Fall Detection With Multimodal Residual Fusion and Pareto-Optimized Client Selection by Bao-Quan Wang, Fan Yang, Yi Wang, Fan Zhao, Yun-Fei Han, Yu-Peng Ma

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AV-Teller: Browser Fingerprinting for Client-Side Security Software Identification by Hyeong-Seok Jang, Mohsen Ali Alawami, Ki-Woong Park

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Detecting Emerging DGA Malware in Federated Environments via Variational Autoencoder-Based Clustering and Resource-Aware Client Selection by Ma Viet Duc, Pham Minh Dang, Tran Thu Phuong, Truong Duc Truong, Vu Hai, Nguyen Huu Thanh

“…To address this, we present FedSAGE, a security-aware federated intrusion detection framework that combines Variational Autoencoder (VAE)-based latent representation learning with unsupervised clustering and resource-efficient client selection. …”
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Nailed Wellness: A Proposal for Training Nail Salons to Detect Signs of Organ System Dysfunction on their Client’s Nails by Maya Morcos, Amir-Ali Golrokhian-Sani, Izzah Wahab

“…Nail salon staff can be trained to recognize red flags on a client’s nails with an emphasis on safely screening clients and referring them to the appropriate healthcare providers when necessary. …”
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Deep Learning Method with Domain-Task Adaptation and Client-Specific Fine-Tuning YOLO11 Model for Counting Greenhouse Tomatoes by Igor Glukhikh, Dmitry Glukhikh, Anna Gubina, Tatiana Chernysheva

Subjects: “…object detection…”
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Comparative Analysis of Machine learning Methods to Identify signs of suspicious Transactions of Credit Institutions and Their Clients by Yu. M. Beketnova

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Molecular Detection of Neiseria Gonorrheae and Chlamydia Trichomatis Among Infertile Male Client Attending some Health Facilities in the Northwest, Nigeria by Dogo Ezekiel Bitet, Abdulhadi Sale Kumurya, Abubakar Isah Aliyu, Bawa Ega, Joseph M Ajagbe

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PPFL-DCS: Privacy-Preserving Federated Learning Using Neural Transformer and Leveraging Dynamic Client Selection to Accommodate Data Diversity by Nakul Mehta, Nitesh Bharot, John G. Breslin, Priyanka Verma

“…To address these challenges, we propose PPFL-DCS, an FL framework that incorporates a weighted mechanism for dynamic client selection, accounting for the performance of each local model and data size of each client in integration with a Neural Transformer System (NTS) that enhances the system‘s robustness against the MIA attacks. …”
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PERCEPTION OF THE CLIENTS’ REGARDING SPECIFIC RADIO DIAGNOSTIC PROCEDURES by N. Kusuma

“…Interventional radiology is used in the detection, diagnosis and treatment of injury and disease. …”
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Class-Hidden Client-Side Watermarking in Federated Learning by Weitong Chen, Chi Zhang, Wei Zhang, Jie Cai

“…Federated learning consists of a central aggregator and multiple clients, forming a distributed structure that effectively protects data privacy. …”
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Balanced coarse-to-fine federated learning for noisy heterogeneous clients by Longfei Han, Ying Zhai, Yanan Jia, Qiang Cai, Haisheng Li, Xiankai Huang

“…However, heterogeneous clients have different deep neural network structures, and these models have different sensitivity to various noise types, the fixed noise-detection based methods may not be effective for each client. …”
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Method of Video-Measurements of Traffic Flow Characteristics at a Road Junction by Marina Yashina, Maria Belova, Alexey Mokhov

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Natural infection of Trypanosoma cruzi in client-owned-dogs from rural Yucatan, Mexico by Jorge Andrés Calderón-Quintal, Maryrose José Escalante-Talavera, Christian Florian Teh-Poot, María Noe Carrera-Campellone, Pedro Pablo Martinez-Vega, Victor Manuel Dzul-Huchim, Landy Magaly Pech-Pisté, Etienne B. Waleckx, Liliana Estefanía Villanueva-Lizama, Jaime Ortega-Lopez, Eric Dumonteil, Julio Vladimir Cruz-Chan

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Introduction of breast density notification within BreastScreen South Australia – Results of an online client survey by Liz Buckley, Carolyn Nickson, Jennifer Stone, Ellen Kerrins, Shona Crabb, David Roder, Michelle Reintals

“…Conclusion: Breast density notification was well received by most clients; however, knowledge about associated risk is considered low. …”
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Passive Remote Monitoring Technologies’ Influence on Home Care Clients’ Ability to Stay Home: Multiprovincial Randomized Controlled Trial by Lorie Donelle, Bradley Hiebert, Grace Warner, Michael Reid, Jennifer Reid, Salimah Shariff, Emily Richard, Sandra Regan, Lori Weeks, Kathleen Ledoux

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Efficacy of a Topical Esafoxolaner, Eprinomectin and Praziquantel Combination Against Most Commonly Found Metazoan Parasites of Client-Owned Cats in Greece by Isaia Symeonidou, Georgios Sioutas, Athanasios I. Gelasakis, Frederic Beugnet, Elias Papadopoulos

“…Feline parasitism affects animals’ health and welfare. Faeces from 472 client-owned cats from Greece were examined to provide updated data on the epizootiology of metazoan endo- and ectoparasites (namely, <i>Toxocara cati</i>, Ancylostomatidae, <i>Dipylidium caninum</i>, lungworms, <i>Toxascaris leonina</i>, <i>Otodectes cynotis</i>, fleas, ticks and <i>Notoedres cati</i>). …”
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Vancomycin-Resistant Enterococci and Its Associated Risk Factors among HIV-Positive and -Negative Clients Attending Dessie Referral Hospital, Northeast Ethiopia by Seid Ali, Martha Alemayehu, Mulat Dagnew, Teklay Gebrecherkos

“…From all isolates, the prevalence of VRE among HIV-positive and -negative clients was 5.9% and 7.4%, respectively. Resistance gentamicin, ampicillin, penicillin, and erythromycin was 37.5%, 34.8%, 34.8%, and 22.3%, respectively. …”
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