DAG-Based Swarm Learning Approach in Healthcare: A Survey by David Gana, Faisal Jamil

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FedBFGCN: A Graph Federated Learning Framework Based on Balanced Channel Attention and Cross-Layer Feature Fusion Convolution by Hefei Wang, Ruichun Gu, Jingyu Wang, Xiaolin Zhang, Hui Wei

Subjects: “…Graph federated learning…”
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A Collaborative Privacy Preserved Federated Learning Framework for Pneumonia Detection using Diverse Chest X-ray Data Silos by Shagun Sharma, Kalpna Guleria

Subjects: “…federated learning…”
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Advancements in training and deployment strategies for AI-based intrusion detection systems in IoT: a systematic literature review by S. Kumar Reddy Mallidi, Rajeswara Rao Ramisetty

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Differentially Private Clustered Federated Load Prediction Based on the Louvain Algorithm by Tingzhe Pan, Jue Hou, Xin Jin, Chao Li, Xinlei Cai, Xiaodong Zhou

Subjects: “…federated learning…”
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AFM-DViT: A framework for IoT-driven medical image analysis by Jiacheng Yang

Subjects: “…Adaptive federated learning…”
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System-Level Digital Twin Modeling for Underwater Wireless IoT Networks by Lei Wang, Lei Yan, Xinbin Li, Song Han

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A Novel Federated & Ensembled Learning-Based Battery State-of-Health Estimation for Connected Electric Vehicles by Praveen Abbaraju, Subrata Kumar Kundu

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Minimal sourced and lightweight federated transfer learning models for skin cancer detection by Vikas Khullar, Prabhjot Kaur, Shubham Gargrish, Anand Muni Mishra, Prabhishek Singh, Manoj Diwakar, Anchit Bijalwan, Indrajeet Gupta

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Optimizing intelligent residential scheduling based on policy black box and adaptive clustering federated deep reinforcement learning by Wei Zhang, Yiyang Li

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Comparative Analysis of Federated and Centralized Learning Systems in Predicting Cellular Downlink Throughput Using CNN by Kukuh Nugroho, Hendrawan, Iskandar

Subjects: “…Federated learning…”
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Advancing UAV Communications: A Comprehensive Survey of Cutting-Edge Machine Learning Techniques by Chenrui Sun, Gianluca Fontanesi, Berk Canberk, Amirhossein Mohajerzadeh, Symeon Chatzinotas, David Grace, Hamed Ahmadi

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Double layer federated security learning architecture for artificial intelligence of things by ZHENG Chengbo, YAN Haonan, FU Caili, ZHANG Dong, LI Hui, WANG Bin

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Enhancing Channel Selection in 5G with Decentralized Federated Multi-Agent Deep Reinforcement Learning by Taghi Shahgholi, Keyhan Khamforoosh, Amir Sheikhahmadi, Sadoon Azizi

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Model-Oriented Training of Coordinators of the Decentralized Control System of Technological Facilities With Resource Interaction by Volodymyr M. Dubovoi, Maria S. Yukhimchuk, Viacheslav V. Kovtun, Krzysztof R. Grochla

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A Clonal Selection Optimization System for Multiparty Secure Computing by Minyu Shi, Yongting Zhang, Huanhuan Wang, Junfeng Hu, Xiang Wu

“…First of all, this process enhances the adaptability and robustness of the federated learning scheme and improves the modeling performance and training efficiency. …”
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A Secure Object Detection Technique for Intelligent Transportation Systems by Jueal Mia, M. Hadi Amini

“…Federated Learning is a decentralized machine learning technique that creates a global model by aggregating local models from multiple edge devices without a need to access the local data. …”
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Distributed training of foundation models for ophthalmic diagnosis by Sina Gholami, Fatema-E Jannat, Atalie Carina Thompson, Sally Shin Yee Ong, Jennifer I. Lim, Theodore Leng, Hamed Tabkhivayghan, Minhaj Nur Alam

“…Here we propose a distributed deep learning framework that integrates self-supervised and domain-adaptive federated learning to enhance the detection of eye diseases from optical coherence tomography images. …”
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Lithium-Ion Battery State of Health Degradation Prediction Using Deep Learning Approaches by Talal Alharbi, Muhammad Umair, Abdulelah Alharbi

“…The decentralized approach effectively balances performance and privacy, highlighting the reliability of federated learning in SoH prediction for lithium-ion batteries.…”
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Distributed cross-learning for equitable federated models - privacy-preserving prediction on data from five California hospitals by Tsung-Ting Kuo, Rodney A. Gabriel, Jejo Koola, Robert T. Schooley, Lucila Ohno-Machado

“…We compared D-CLEF with centralized/siloed/federated learning in horizontal or vertical scenarios. …”
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