--- title: "“federated learning”: 23 recent papers (last 30 days)" description: "The keyword “federated learning” appeared in 23 papers in the last 30 days (2026-09-27 to 2026-10-26), ranking #18 of 12,658 keywords." url: https://scholar4ai.com/keywords/federated-learning markdown_url: https://scholar4ai.com/keywords/federated-learning.md license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/) share_text: "The keyword “federated learning” appeared in 23 papers in the last 30 days (2026-09-27 to 2026-10-26), ranking #18 of 12,658 keywords." data_url: https://scholar4ai.com/data/rankings.json updated: 2026-10-26T14:32:25+09:00 window: 2026-09-27 to 2026-10-26 source: OpenAlex (CC0) --- # Papers on “federated learning” - 4 papers in the last 7 days Back to the ranking: https://scholar4ai.com/rankings/keywords.md ## Key findings - The keyword “federated learning” appeared in 23 papers in the last 30 days (2026-09-27 to 2026-10-26), ranking #18 of 12,658 keywords. - Most of them appeared in Neural Computing and Applications (4), Pattern Recognition (3) and Applied Intelligence (2). - Leading institutions: Xidian University (2), Universidade Estadual Paulista (Unesp) (1) and Brock University (1). - Countries: China (9), Singapore (2) and Pakistan (2). - Often studied together with “privacy-preserving machine learning” (6), “privacy preservation” (4) and “data heterogeneity” (3). ## Research ideas that follow this keyword - [Federated learning against money laundering](https://scholar4ai.com/ideas/federated-learning-against-money-laundering.md) *Papers, newest first.* | Date | Title | Journal | Authors | Affiliations | |---|---|---|---|---| | 2026-10-23 | [Multimodal continual federated learning for human activity recognition: Trends and challenges](https://doi.org/10.1007/s10489-026-07562-x) | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | André L. S. Conde, Rodolfo I. Meneguette, Robson E. De Grande et al. | Universidade Estadual Paulista (Unesp); Institute of Mathematics and Computer Science; Brock University | | 2026-10-23 | [Synergizing Disentangled Representation and Split Federated Learning to Break Centralized Bottlenecks in Gaze Estimation](https://doi.org/10.1007/s11263-026-03084-8) | [International Journal of Computer Vision](https://scholar4ai.com/journals/international-journal-of-computer-vision.md) | Yunlong Wang, Zhengquan Luo, Kunbo Zhang et al. | Nanyang Technological University; Chinese Academy of Sciences; Institute of Automation; … | | 2026-10-22 | [Survey on intelligent video surveillance](https://doi.org/10.1186/s40537-026-01560-z) | [Journal Of Big Data](https://scholar4ai.com/journals/journal-of-big-data.md) | Mahmoud Elnady, Hossam E. Abdelmunim, Diaa Abidou et al. | Ain Shams University | | 2026-10-21 | [Toward unified intelligence in renewable-dominant power systems: a survey of hybrid forecasting, explainable AI, and multi-agent optimization](https://doi.org/10.1007/s10462-026-11711-6) | [Artificial Intelligence Review](https://scholar4ai.com/journals/artificial-intelligence-review.md) | Tariq Ali, Muzna Sarwar, Husam S. Samkari et al. | University of Tabuk; Islamia University of Bahawalpur; Najran University | | 2026-10-16 | [FedSTHG: A federated learning framework via spatiotemporal hypergraphs for multi-center brain disease diagnosis](https://doi.org/10.1016/j.eswa.2026.134766) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Xinyue Yu, Yating Li, Zhenhao Chu et al. | | | 2026-10-15 | [Platform-free proof of federated learning consensus blockchain with optimized neural network-based electronic health records storage and sharing scheme](https://doi.org/10.1007/s10115-026-02894-z) | [Knowledge and Information Systems](https://scholar4ai.com/journals/knowledge-and-information-systems.md) | M Lavanya | | | 2026-10-14 | [Mitigating domain shift in federated learning via intra- and inter-domain prototypes](https://doi.org/10.1016/j.patcog.2026.115131) | [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | Huy Q. Le, Ye Lin Tun, Yu Qiao et al. | Kyung Hee University; University of Da Nang; Viện Khoa học và Công nghệ số | | 2026-10-14 | [Federated multi-view information fusion based on prototype and feature distillation: A privacy-performance tradeoff method for data heterogeneity](https://doi.org/10.1016/j.inffus.2026.104859) | [Information Fusion](https://scholar4ai.com/journals/information-fusion.md) | Hui Lin, Tianyi Zhou, Jingjing Guo et al. | Fujian Normal University; Xidian University; Xi'an Jiaotong University | | 2026-10-13 | [Unified dual-decoder autoencoder for federated multi-modal clustering with arbitrary modality missingness](https://doi.org/10.1016/j.neunet.2026.109746) | [Neural Networks](https://scholar4ai.com/journals/neural-networks.md) | Zhenhao Xu, Huazhu Chen, Weiwei Wang et al. | Zhongyuan University of Technology; Xidian University; Xinzhou Teachers University | | 2026-10-10 | [BANAR: Self-feedback stealthy backdoor attack in federated learning](https://doi.org/10.1016/j.knosys.2026.117157) | [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | Yu Qiao Deng, Yuling Mu, Songlei Wang et al. | Yanshan University | | 2026-10-10 | [Comprehensive review of GANs and diffusion models in multimodal 3D medical image conversion](https://doi.org/10.1016/j.patcog.2026.115079) | [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | Nan Jiang, 李皓塱, Xiao Teng et al. | Central South University; Hunan Children's Hospital; National University of Defense Technology; … | | 2026-10-09 | [Mitigating data reconstruction attacks in federated learning: Conditions, bounds, and theoretical guarantees](https://doi.org/10.1016/j.artint.2026.104641) | [Artificial Intelligence](https://scholar4ai.com/journals/artificial-intelligence.md) | Xiaojin Zhang, Mingcong Xu, Wei Chen et al. | Huazhong University of Science and Technology; Hong Kong Polytechnic University | | 2026-10-03 | [An intelligent cybersecurity framework for banking systems: integrating neural networks, large language models, and federated learning for anti-money laundering and fraud detection](https://doi.org/10.1186/s43093-026-01015-0) | [Future Business Journal](https://scholar4ai.com/journals/future-business-journal.md) | Tanvir Sajid, Sajida Hafeez | National Textile University | | 2026-10-02 | [A privacy-preserving distributed credit evaluation model based on blockchain](https://doi.org/10.1007/s10479-026-07436-y) | [Annals of Operations Research](https://scholar4ai.com/journals/annals-of-operations-research.md) | Xianhua Kuang, Chaoqun Ma, Konstantinos N. Baltas et al. | University of Essex; University of Auckland | | 2026-10-01 | [FedLDC-Lite: Lightweight counterfactual learning for federated multimodal object detection at the edge](https://doi.org/10.1016/j.patcog.2026.115203) | [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | Xiuheng Liao, Weiyu Wang, Ziang Wu et al. | University of Aizu; Hosei University; La Trobe University | | 2026-10-01 | [OADPC-HSSA: Observation-Aware Dynamic Privacy Control for Federated Learning with Hierarchical Shuffling and Secure Aggregation](https://doi.org/10.1016/j.eswa.2026.134828) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Jianli Xie, Zishan Wu, Bing Bu et al. | Lanzhou Jiaotong University; Beijing Jiaotong University | | 2026-10-01 | [A heterogeneity-aware federated explainable visual learning framework for robust email malware detection](https://doi.org/10.1007/s00521-026-12523-z) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | G. Shanmuga Priya, V. R. S. Mani, C. Chellaswamy | | | 2026-10-01 | [Privacy protection of multi-source data combined with homomorphic encryption within a federated learning framework](https://doi.org/10.1007/s00521-026-12594-y) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Shuhui Wang, Xiaochen Jia | Shandong University of Art and Design; Shandong University | | 2026-10-01 | [MMT: Achieving Exact Federated Unlearning with Improved Post-unlearning Performance](https://doi.org/10.1007/s10994-026-07117-8) | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | Z. Zhang, Nhung Bui, Arun Verma et al. | National University of Singapore; Singapore-MIT Alliance for Research and Technology | | 2026-10-01 | [FedGPA: a multi-level aggregation framework for federated learning with collaborative optimization of generalization and personalization](https://doi.org/10.1007/s10489-026-07354-3) | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | Zigang Chen, Haichong Jiang, Deyang Xie et al. | Chongqing University of Posts and Telecommunications | | 2026-10-01 | [FedHybrid-ViT: A Federated-Hybrid Framework for Generalizable Deepfake Detection on Heterogeneous Data](https://doi.org/10.1007/s10994-026-07171-2) | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | Abdullah Mohammad Sakib, Md. Mohashin Hossain, Mahedy Hasan Foysal et al. | International University of Business Agriculture and Technology; Dhaka International University; United International University | | 2026-10-01 | [Bio-inspired reinforcement federated optimization for energy-aware and privacy-preserving healthcare systems](https://doi.org/10.1007/s00521-026-12377-5) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Geetha G, N. Ramshankar | Saveetha University | | 2026-10-01 | [Collaborative encryption-based machine learning model training method and data protection software system construction](https://doi.org/10.1007/s00521-026-12383-7) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Dianqing Bao, Wen Su | | ## Cite this page Scholar4AI. (2026). Papers on “federated learning”. Journal Research Rankings. Retrieved October 26, 2026, from https://scholar4ai.com/keywords/federated-learning ```bibtex @misc{scholar4ai-keywords-federated-learning, author = {{Scholar4AI}}, title = {Papers on “federated learning”}, year = {2026}, howpublished = {\url{https://scholar4ai.com/keywords/federated-learning}}, urldate = {2026-10-26}, note = {Journal Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0} } ``` License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).