Relevant bibliographies by topics / Decentralized federated learning

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Decentralized federated learning'

Author: Grafiati
Published: 5 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Decentralized federated learning"

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Jiang, Jingyan, Liang Hu, Chenghao Hu, Jiate Liu, and Zhi Wang. "BACombo—Bandwidth-Aware Decentralized Federated Learning." Electronics 9, no. 3 (2020): 440. http://dx.doi.org/10.3390/electronics9030440.

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The emerging concern about data privacy and security has motivated the proposal of federated learning. Federated learning allows computing nodes to only synchronize the locally- trained models instead of their original data in distributed training. Conventional federated learning architecture, inherited from the parameter server design, relies on highly centralized typologies and large nodes-to-server bandwidths. However, in real-world federated learning scenarios, the network capacities between nodes are highly uniformly distributed and smaller than that in data centers. As a result, how to e
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Srinivasa Rao, Angajala. "Unifying Intelligence: Federated Learning in Cloud Environments for Decentralized Machine Learning." International Journal of Science and Research (IJSR) 12, no. 12 (2023): 997–99. http://dx.doi.org/10.21275/sr231212134726.

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Bonawitz, Kallista, Peter Kairouz, Brendan Mcmahan, and Daniel Ramage. "Federated learning and privacy." Communications of the ACM 65, no. 4 (2022): 90–97. http://dx.doi.org/10.1145/3500240.

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Jitendra Singh Chouhan, Amit Kumar Bhatt, Nitin Anand. "Federated Learning; Privacy Preserving Machine Learning for Decentralized Data." Tuijin Jishu/Journal of Propulsion Technology 44, no. 1 (2023): 167–69. http://dx.doi.org/10.52783/tjjpt.v44.i1.2234.

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 Federated learning represents a compelling solution for tackling the privacy challenges inherent in decentralized and distributed environments when it comes to machine learning. This scholarly paper delves deep into the realm of federated learning, encompassing its applications and the latest privacy-preserving techniques used for training machine learning models in a decentralized manner. We explore the reasons behind the adoption of federated learning, highlight its advantages over conventional centralized approaches, and examine the diverse methods employed to sa
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Woo, Gimoon, Hyungbin Kim, Seunghyun Park, Cheolwoo You, and Hyunhee Park. "Fairness-Based Multi-AP Coordination Using Federated Learning in Wi-Fi 7." Sensors 22, no. 24 (2022): 9776. http://dx.doi.org/10.3390/s22249776.

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Federated learning is a type of distributed machine learning in which models learn by using large-scale decentralized data between servers and devices. In a short-range wireless communication environment, it can be difficult to apply federated learning because the number of devices in one access point (AP) is small, which can be small enough to perform federated learning. Therefore, it means that the minimum number of devices required to perform federated learning cannot be matched by the devices included in one AP environment. To do this, we propose to obtain a uniform global model regardless
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Ahmed Al-Banori and Layla Hassan. "Federated Learning: Decentralized Machine Learning for Privacy Preservation." International Journal of Emerging Trends in Information Technology (IJEIT) 1, no. 1 (2025): 61–72. https://doi.org/10.64056/n2nbva22.

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Federated Learning (FL) is an emerging paradigm in machine learning that enables multiple devices or organizations to collaboratively train a global model without sharing raw data​. This decentralized approach addresses data silo and privacy challenges by ensuring sensitive information remains local to each participant. In this article, we survey recent advances in FL and propose a hypothetical experiment comparing federated and centralized learning on a standard dataset. Our experimental methodology employs the Federated Averaging algorithm​ across simulated clients and measures model accurac
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Ajay, Ajay, Ajay Kumar, Krishan Kant Singh Gautam, Pratibha Deshmukh, Pavithra G, and Laith Abualigah. "Collaborative Intelligence for IoT: Decentralized Net security and confidentiality." Journal of Intelligent Systems and Internet of Things 13, no. 2 (2024): 202–11. http://dx.doi.org/10.54216/jisiot.130216.

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This research compares federated and centralized learning paradigms to discover the best machine learning privacy-model accuracy balance. Federated learning allows model training across devices or clients without data centralization. It's innovative distributed machine learning. Keeping data on individual devices reduces the hazards of centralized data storage, improving user privacy and security. However, centralized learning concentrates data on a server, which raises privacy and security problems. It evaluates two learning approaches using simulated data in a simple regression problem frame
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Monteiro, Daryn, Ishaan Mavinkurve, Parth Kambli, and Prof Sakshi Surve. "Federated Learning for Privacy-Preserving Machine Learning: Decentralized Model Training with Enhanced Data Security." International Journal for Research in Applied Science and Engineering Technology 12, no. 11 (2024): 355–61. http://dx.doi.org/10.22214/ijraset.2024.65062.

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Abstract: Artificial Intelligence has found widespread use across various industries, from optimizing manufacturing workflows to diagnosing health conditions. However, the large volumes of data required to train AI models raise privacy concerns, especially when stored in centralized databases vulnerable to leaks. Federated Learning solves this problem by training models collaboratively by avoiding centralization of the sensitive data, preserving privacy while allowing decentralized models to be exported to edge devices. This paper explores Federated Learning, focusing on its technical aspects,
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Jiang, Changsong, Chunxiang Xu, Chenchen Cao, and Kefei Chen. "GAIN: Decentralized Privacy-Preserving Federated Learning." Journal of Information Security and Applications 78 (November 2023): 103615. http://dx.doi.org/10.1016/j.jisa.2023.103615.

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Qi, Lu, Haoze Chen, Hongliang Zou, Shaohua Chen, Xiaoying Zhang, and Hongyan Chen. "Decentralized Federated Learning with Prototype Exchange." Mathematics 13, no. 2 (2025): 237. https://doi.org/10.3390/math13020237.

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As AI applications become increasingly integrated into daily life, protecting user privacy while enabling collaborative model training has become a crucial challenge, especially in decentralized edge computing environments. Traditional federated learning (FL) approaches, which rely on centralized model aggregation, struggle in such settings due to bandwidth limitations, data heterogeneity, and varying device capabilities among edge nodes. To address these issues, we propose PearFL, a decentralized FL framework that enhances collaboration and model generalization by introducing prototype exchan
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Dissertations / Theses on the topic "Decentralized federated learning"

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Adapa, Supriya. "TensorFlow Federated Learning: Application to Decentralized Data." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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Machine learning is a complex discipline. But implementing machine learning models is far less daunting and difficult than it used to be, thanks to machine learning frameworks such as Google’s TensorFlow Federated that ease the process of acquiring data, training models, serving predictions, and refining future results. There are an estimated 3 billion smartphones in the world and 7 billion connected devices. These phones and devices are constantly generating new data. Traditional analytics and machine learning need that data to be centrally collected before it is processed to yield insights,
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Mäenpää, Dylan. "Towards Peer-to-Peer Federated Learning: Algorithms and Comparisons to Centralized Federated Learning." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176778.

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Due to privacy and regulatory reasons, sharing data between institutions can be difficult. Because of this, real-world data are not fully exploited by machine learning (ML). An emerging method is to train ML models with federated learning (FL) which enables clients to collaboratively train ML models without sharing raw training data. We explored peer-to-peer FL by extending a prominent centralized FL algorithm called Fedavg to function in a peer-to-peer setting. We named this extended algorithm FedavgP2P. Deep neural networks at 100 simulated clients were trained to recognize digits using Feda
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Bagheri, Behrad. "Decentralized Federated Autonomous Organizations for Prognostics and Health Management." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1592133991337126.

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Lundberg, Oskar. "Decentralized machine learning on massive heterogeneous datasets : A thesis about vertical federated learning." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-444639.

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The need for a method to create a collaborative machine learning model which can utilize data from different clients, each with privacy constraints, has recently emerged. This is due to privacy restrictions, such as General Data Protection Regulation, together with the fact that machine learning models in general needs large size data to perform well. Google introduced federated learning in 2016 with the aim to address this problem. Federated learning can further be divided into horizontal and vertical federated learning, depending on how the data is structured at the different clients. Vertic
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Vikström, Johan. "Comparing decentralized learning to Federated Learning when training Deep Neural Networks under churn." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-300391.

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Decentralized Machine Learning could address some problematic facets with Federated Learning. There is no central server acting as an arbiter of whom or what may benefit from Machine Learning models created by the vast amount of data becoming available in recent years. It could also increase the reliability and scalability of Machine Learning systems thereby drawing the benefit of having more data accessible. Gossip Learning is such a protocol, but has primarily been designed with linear models in mind. How does Gossip Learning perform when training Deep Neural Networks? Could it be a viable a
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Book chapters on the topic "Decentralized federated learning"

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Chaudhari, Anagha N., A. A. Hitham Seddig, Roshani Raut, and Aliza Sarlan. "Federated Learning." In Decentralized Healing. CRC Press, 2025. https://doi.org/10.1201/9781003546559-6.

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Pardeshi, Komal Rahul, Anita Mukund Pujar, and Yang Li. "Fundamentals of Blockchain and Federated Learning." In Decentralized Healing. CRC Press, 2025. https://doi.org/10.1201/9781003546559-4.

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Yadav, Gitanjali Bhimrao, and Jayashri Bagade. "Secure Federated Learning Framework for Training Deep Learning Models." In Decentralized Healing. CRC Press, 2025. https://doi.org/10.1201/9781003546559-8.

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Vyavahare, Arati J. "Revolutionizing Healthcare Systems with Federated Learning and Blockchain." In Decentralized Healing. CRC Press, 2025. https://doi.org/10.1201/9781003546559-12.

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Zec, Edvin Listo, Ebba Ekblom, Martin Willbo, Olof Mogren, and Sarunas Girdzijauskas. "Decentralized Adaptive Clustering of Deep Nets is Beneficial for Client Collaboration." In Trustworthy Federated Learning. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-28996-5_5.

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Patil, Sonali Dhananjay, and Niharika Pagare. "Foundation of Blockchain and Federated Learning for Secure and Decentralized Data Management." In Decentralized Healing. CRC Press, 2025. https://doi.org/10.1201/9781003546559-3.

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Shinde, Rucha, Rachana Patil, and Arijit Karati. "Securing and Managing Data in Federated Learning for Healthcare Cyber-Physical Systems." In Decentralized Healing. CRC Press, 2025. https://doi.org/10.1201/9781003546559-13.

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Siniosoglou, Ilias, Stamatia Bibi, Konstantinos-Filippos Kollias, et al. "Federated Learning Models in Decentralized Critical Infrastructure." In Shaping the Future of IoT with Edge Intelligence. River Publishers, 2023. http://dx.doi.org/10.1201/9781032632407-7.

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Thabet, Saqr Khalil Saeed, Behnaz Soltani, Yipeng Zhou, Quan Z. Sheng, and Shiting Wen. "Towards Efficient Decentralized Federated Learning: A Survey." In Lecture Notes in Computer Science. Springer Nature Singapore, 2024. https://doi.org/10.1007/978-981-96-0814-0_14.

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Hegedűs, István, Gábor Danner, and Márk Jelasity. "Gossip Learning as a Decentralized Alternative to Federated Learning." In Distributed Applications and Interoperable Systems. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22496-7_5.

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Conference papers on the topic "Decentralized federated learning"

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Zhang, Kaichuang, Ping Xu, and Zhi Tian. "Decentralized Federated Learning for Meta Computing." In 2024 International Conference on Meta Computing (ICMC). IEEE, 2024. https://doi.org/10.1109/icmc60390.2024.00029.

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Zhang, Hongyi, Jan Bosch, and Helena Holmström Olsson. "EdgeFL: A Lightweight Decentralized Federated Learning Framework." In 2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC). IEEE, 2024. http://dx.doi.org/10.1109/compsac61105.2024.00081.

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Guo, Jian, Hengyu Mu, Hengyi Ren, Chong Han, and Lijuan Sun. "Decentralized Federated Learning Links for Biometric Recognition." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10650816.

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Abiru, Shouta, and Keiichi Tamura. "DFedMD: Decentralized Federated Learning using Model Distillation." In 2024 Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems (SCIS&ISIS). IEEE, 2024. https://doi.org/10.1109/scisisis61014.2024.10759998.

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Lee, Chaehyeon, Jonathan Heiss, Stefan Tai, and James Won-Ki Hong. "End-to-End Verifiable Decentralized Federated Learning." In 2024 IEEE International Conference on Blockchain and Cryptocurrency (ICBC). IEEE, 2024. http://dx.doi.org/10.1109/icbc59979.2024.10634412.

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Zhang, Kaichuang, Ping Xu, and Zhi Tian. "Distributional and Byzantine Robust Decentralized Federated Learning." In 2025 59th Annual Conference on Information Sciences and Systems (CISS). IEEE, 2025. https://doi.org/10.1109/ciss64860.2025.10944744.

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Pozzo, Antonella Del, and Maxence Perion. "Decentralized Federated Learning: Enhancing Reliability with Blockchain." In 2025 55th Annual IEEE/IFIP International Conference on Dependable Systems and Networks - Supplemental Volume (DSN-S). IEEE, 2025. https://doi.org/10.1109/dsn-s65789.2025.00061.

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Han, Jialiang, Yudong Han, Ying Zhang, Xiang Jing, and Yun Ma. "Demystifying Swarm Learning: An Emerging Decentralized Federated Learning System." In 2024 IEEE 24th International Symposium on Cluster, Cloud and Internet Computing (CCGrid). IEEE, 2024. http://dx.doi.org/10.1109/ccgrid59990.2024.00049.

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Zhang, Long, Shuang Qin, Gang Feng, and Youkun Peng. "Decentralized Federated Learning Under Free-riders: Credibility Analysis." In IEEE INFOCOM 2024 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). IEEE, 2024. http://dx.doi.org/10.1109/infocomwkshps61880.2024.10620869.

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Hu, Jifei, Yanli Li, Lifa Liu, and Hua Lou. "DFedCL: Decentralized Federated Collaborative Learning with Privacy Protection." In 2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2024. https://doi.org/10.1109/smc54092.2024.10831860.

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Reports on the topic "Decentralized federated learning"

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Pasupuleti, Murali Krishna. Next-Generation Extended Reality (XR): A Unified Framework for Integrating AR, VR, and AI-driven Immersive Technologies. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv325.

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Abstract: Extended Reality (XR), encompassing Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR), is evolving into a transformative technology with applications in healthcare, education, industrial training, smart cities, and entertainment. This research presents a unified framework integrating AI-driven XR technologies with computer vision, deep learning, cloud computing, and 5G connectivity to enhance immersion, interactivity, and scalability. AI-powered neural rendering, real-time physics simulation, spatial computing, and gesture recognition enable more realistic and adap
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Pasupuleti, Murali Krishna. Securing AI-driven Infrastructure: Advanced Cybersecurity Frameworks for Cloud and Edge Computing Environments. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv225.

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Abstract: The rapid adoption of artificial intelligence (AI) in cloud and edge computing environments has transformed industries by enabling large-scale automation, real-time analytics, and intelligent decision-making. However, the increasing reliance on AI-powered infrastructures introduces significant cybersecurity challenges, including adversarial attacks, data privacy risks, and vulnerabilities in AI model supply chains. This research explores advanced cybersecurity frameworks tailored to protect AI-driven cloud and edge computing environments. It investigates AI-specific security threats,
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