Federated Intelligence Architectures for Secure, Data-Driven Innovation Across AI, IoT, and Cloud Ecosystems
DOI:
https://doi.org/10.70063/techcompinnovations.v2i2.124Keywords:
Federated Intelligence, IoT Ecosystems, Blockchain-enabled Security, Zero-Trust Architecture, Edge AIAbstract
This study examines the emerging paradigm of federated intelligence architectures as a secure, privacy-preserving, and scalable foundation for data-driven innovation across AI, IoT, and cloud ecosystems. With billions of interconnected devices generating massive heterogeneous data, traditional centralized machine-learning models face critical limitations, including privacy risks, regulatory constraints, latency, and single points of failure. Through a qualitative content-analysis approach, this paper synthesizes contemporary research on federated learning, blockchain integration, zero-trust governance, and edge intelligence to formulate a comprehensive understanding of distributed AI infrastructures. The findings highlight that federated learning enables collaborative model training without exposing raw data, significantly enhancing privacy, security, and compliance. Moreover, combining blockchain with federated learning strengthens auditability, model integrity, and trust, while zero-trust principles provide continuous verification and adaptive security enforcement across devices. Edge-AI integration further reduces latency and bandwidth consumption, enabling real-time analytics in resource-constrained IoT environments. Collectively, these elements contribute to the formation of cognitive ecosystems capable of autonomous, interoperable, and context-aware operations. The study underscores the transformative potential of federated intelligence while identifying critical gaps that inform future research trajectories.
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Abbas, S., Abbas, Z., Zahir, A., & Lee, S. W. (2024). Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration [Review of Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration]. Healthcare, 12(24), 2587. Multidisciplinary Digital Publishing Institute. https://doi.org/10.3390/healthcare12242587
Adame, T., Amri, E., Antonopoulos, G., Azaiez, S., Berne, A., Camargo, J. S., Kakoulidis, H., Kleisarchaki, S., Llamedo, A., Prasinos, M., Psara, K., & Shumaiev, K. (2024). Presenting the COGNIFOG Framework: Architecture, Building Blocks and Road toward Cognitive Connectivity. Sensors, 24(16), 5283. https://doi.org/10.3390/s24165283
Aden, H., Çetin, M. Ç., Chatzimisios, P., Falch, M., Ferreira, A., Gómez‐Barroso, J. L., Moritz, M., Özparlak, B. O., & Kanaki, E. (2025). WG5 : Legal factors in cybersecurity for wireless systems: a vertical approach. Research Portal Denmark, 67.
Aggarwal, M., Khullar, V., & Goyal, N. (2024). A comprehensive review of federated learning: Methods, applications, and challenges in privacy-preserving collaborative model training [Review of A comprehensive review of federated learning: Methods, applications, and challenges in privacy-preserving collaborative model training]. CRC Press eBooks, 570. Informa. https://doi.org/10.1201/9781003471059-73
Ahmad, W., Almaiah, M. A., Ali, B. S., & Ali, A. (2024). A Privacy Preserving Federated Learning BasedIoT Framework Using Cloud Computing. Research Square (Research Square). https://doi.org/10.21203/rs.3.rs-4701071/v1
Ajish, D. (2024). The significance of artificial intelligence in zero trust technologies: a comprehensive review [Review of The significance of artificial intelligence in zero trust technologies: a comprehensive review]. Journal of Electrical Systems and Information Technology, 11(1). Springer Science+Business Media. https://doi.org/10.1186/s43067-024-00155-z
Akbarfam, A. J., & Maleki, H. (2024). SOK: Blockchain for Provenance. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2407.17699
Akther, A., Arobee, A., Adnan, A. A., Auyon, O., Islam, A. J., & Akter, F. (2025). Blockchain As a Platform For Artificial Intelligence (AI) Transparency. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2503.08699
Albelaihi, R. (2025). Mobility Prediction and Resource-Aware Client Selection for Federated Learning in IoT. Future Internet, 17(3), 109. https://doi.org/10.3390/fi17030109
Albshaier, L., Almarri, S., & Albuali, A. (2025). Federated Learning for Cloud and Edge Security: A Systematic Review of Challenges and AI Opportunities [Review of Federated Learning for Cloud and Edge Security: A Systematic Review of Challenges and AI Opportunities]. Electronics, 14(5), 1019. Multidisciplinary Digital Publishing Institute. https://doi.org/10.3390/electronics14051019
Alsulaimawi, Z. (2024). Securing Federated Learning with Control-Flow Attestation: A Novel Framework for Enhanced Integrity and Resilience against Adversarial Attacks. https://doi.org/10.48550/ARXIV.2403.10005
Arjomandi, F., Alamouti, S., Burger, M., & Altakrouri, B. (2024). Building Blocks to Empower Cognitive Internet with Hybrid Edge Cloud. In Lecture notes in networks and systems (p. 534). Springer International Publishing. https://doi.org/10.1007/978-3-031-73125-9_34
Bachlechner, D., Hetfleisch, R. H., Krenn, S., Lorünser, T., & Rader, M. (2024). Protecting Privacy in Federated Time Series Analysis: A Pragmatic Technology Review for Application Developers. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2408.15694
Barbosa, A. V., Ngo, T. B. T., Jafarigol, E., Trafali̇s, T. B., & Ojoboh, E. P. (2025). Using Federated Machine Learning in Predictive Maintenance of Jet Engines. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2502.05321
Barros, A., Veiga, R., Morais, R., Rosário, D., & Cerqueira, E. (2024). MESFLA: Model Efficiency through Selective Federated Learning Algorithm. Journal of Internet Services and Applications, 15(1), 495. https://doi.org/10.5753/jisa.2024.4044
Beltrán, E. T. M., Gómez, Á. L. P., Feng, C., Sánchez, P. M. S., Bernal, S. L., Bovet, G., Pérez, M. G., Pérez, G. M., & Celdrán, A. H. (2023). Fedstellar: A Platform for Decentralized Federated Learning. Expert Systems with Applications, 242, 122861. https://doi.org/10.1016/j.eswa.2023.122861
Bhushan, B., Rajgopal, P. R., & Sharma, K. C. (2025). An Intent-Aware Zero Trust Identity Architecture for Unifying Human and Machine Access. International Journal of Computational and Experimental Science and Engineering, 11(3). https://doi.org/10.22399/ijcesen.3886
Bonthu, C., & Goel, G. (2025). Autonomous Supplier Evaluation and Data Stewardship with AI: Building Transparent and Resilient Supply Chains. International Journal of Computational and Experimental Science and Engineering, 11(3). https://doi.org/10.22399/ijcesen.3854
Boruga, D., Bolintineanu, D., & Racates, G. I. (2024). Federated learning in edge computing: Enhancing data privacy and efficiency in resource-constrained environments. World Journal of Advanced Engineering Technology and Sciences, 13(2), 205. https://doi.org/10.30574/wjaets.2024.13.2.0563
Cai, Z., Chen, J., Fan, Y., Zheng, Z., & Li, K. (2024). Blockchain-empowered Federated Learning: Benefits, Challenges, and Solutions. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2403.00873
Campos, E. M., González-Vidal, A., Hernández-Ramos, J. L., & Skármeta, A. (2024). FedRDF: A Robust and Dynamic Aggregation Function against Poisoning Attacks in Federated Learning. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2402.10082
Cheikh, I., Roy, S., Sabir, E., & Aouami, R. (2025). Energy, Scalability, Data and Security in Massive IoT: Current Landscape and Future Directions. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2505.03036
Chen, C., Liu, J., Tan, H., Li, X., Wang, K. I., Li, P., Sakurai, K., & Dou, D. (2024). Trustworthy federated learning: privacy, security, and beyond. Knowledge and Information Systems, 67(3), 2321. https://doi.org/10.1007/s10115-024-02285-2
Chowdhury, R. H. (2024). Blockchain and AI: Driving the future of data security and business intelligence. World Journal of Advanced Research and Reviews, 23(1), 2559. https://doi.org/10.30574/wjarr.2024.23.1.2273
Commey, D., Hounsinou, S., & Crosby, G. V. (2024). Securing Health Data on the Blockchain: A Differential Privacy and Federated Learning Framework. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2405.11580
Dash, B. (2024). Zero-Trust Architecture (ZTA): Designing an AI-Powered Cloud Security Framework for LLMs’ Black Box Problems. 4(2), 1. https://doi.org/10.54026/ctes/1058
Deshmukh, A., Rosa, J. L. de la, Rodriguez, R. V., & Dasari, S. (2025). Enhancing Privacy in IoT-Enabled Digital Infrastructure: Evaluating Federated Learning for Intrusion and Fraud Detection. Sensors, 25(10), 3043. https://doi.org/10.3390/s25103043
Dhinakaran, D., Sankar, S. M. U., Selvaraj, D., & Raja, S. E. (2024). Privacy-Preserving Data in IoT-based Cloud Systems: A Comprehensive Survey with AI Integration. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2401.00794
Dutta, S., Freitas, I. L. de, Xavier, P. M., Farias, C. M. de, & Bernal, D. E. (2024). Federated Learning in Chemical Engineering: A Tutorial on a Framework for Privacy-Preserving Collaboration Across Distributed Data Sources. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2411.16737
Erdal, Ş., Karakoç, F., & Ozdemır, E. (2024). A Survey On Security and Privacy Aspects and Solutions for Federated Learning in Mobile Communication Networks. DergiPark (Istanbul University). https://dergipark.org.tr/tr/pub/itujwcc/issue/87385/1482654
Feng, C., Celdrán, A. H., Assen, J. von der, Beltrán, E. T. M., Bovet, G., & Stiller, B. (2024). DART: A Solution for decentralized federated learning model robustness analysis. Array, 23, 100360. https://doi.org/10.1016/j.array.2024.100360
Friedrich, C., Vogt, S., Rudolph, F. B., Patolla, P., Grützmann, J. M., Hohmeier, O., Richter, M., Wenzel, K., Reichelt, D., & Ihlenfeldt, S. (2024). Enabling Federated Learning Services Using OPC UA, Linked Data and GAIA-X in Cognitive Production. Journal of Machine Engineering, 24(2), 18. https://doi.org/10.36897/jme/188618
Gao, Y., Kim, M., Abuadbba, A., Kim, Y., Thapa, C., Kim, K., Camtep, S. A., Kim, H., & Nepal, Surya. (2020). End-to-End Evaluation of Federated Learning and Split Learning for Internet of Things. https://doi.org/10.1109/srds51746.2020.00017
Garg, M. (2025). Adaptive Differential Privacy in Federated Edge AI for Medical IoT: Energy-Efficient, HIPAA-Compliant Frameworks for Distributed Real-Time Diagnostics. Journal of Information Systems Engineering & Management, 10, 36. https://doi.org/10.52783/jisem.v10i34s.5773
Goh, E., Kim, D., Kim, D.-Y., & Lee, K. (2023). Blockchain-Enabled Federated Learning: A Reference Architecture Design, Implementation, and Verification. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2306.10841
Hasan, M. (2024). Enhancing Enterprise Security with Zero Trust Architecture. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2410.18291
Hayouni, H., & Nasraoui, L. (2025). NAIDS4IoT: A Novel Artificial Intelligence-Based Intrusion Detection Architecture for the Internet of Things. Inteligencia Artificial, 28(76), 253. https://doi.org/10.4114/intartif.vol28iss76pp253-282
Himeur, Y., Sayed, A. N., Alsalemi, A., Bensaali, F., & Amira, A. (2023). Edge AI for Internet of Energy: Challenges and perspectives. Internet of Things, 25, 101035. https://doi.org/10.1016/j.iot.2023.101035
Huang, H., Iskandarov, B., Rahman, M., Otal, H. T., & Canbaz, M. A. (2024). Federated Learning in Adversarial Environments: Testbed Design and Poisoning Resilience in Cybersecurity. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2409.09794
Hussain, M., Pal, S., Jadidi, Z., Foo, E., & Kanhere, S. S. (2024). Federated Zero Trust Architecture using Artificial Intelligence. IEEE Wireless Communications, 31(2), 30. https://doi.org/10.1109/mwc.001.2300405
Jasim, Z. A., & Hadi, A. K. (2025). Optimizing Blockchain Network Performance Using Blake3 Hash Function in POS Consensus Algorithm. IEEE Access, 1. https://doi.org/10.1109/access.2025.3546723
Javed, F., Zeydan, E., Mangues‐Bafalluy, J., Dev, K., & Blanco, L. (2025). Blockchain for Federated Learning in the Internet of Things: Trustworthy Adaptation, Standards, and the Road Ahead. IEEE Communications Standards Magazine, 1. https://doi.org/10.1109/mcomstd.2025.3593809
Kang, J., Xiong, Z., Jiang, C., Liu, Y., Guo, S., Zhang, Y., Niyato, D., Leung, C., & Miao, C. (2022). Scalable and Communication-efficient Decentralized Federated Edge Learning with Multi-blockchain Framework. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2008.04743
Karamchand, G. (2024). Zero trust and AI: A synergistic approach to next-generation cyber threat mitigation. World Journal of Advanced Research and Reviews, 24(3), 3374. https://doi.org/10.30574/wjarr.2024.24.3.3883
K.M., S., Nicolazzo, S., Arazzi, M., Nocera, A., A., R. R. K., Vinod, P., & Conti, M. (2024). Privacy-preserving in Blockchain-based Federated Learning systems. Computer Communications, 222, 38. https://doi.org/10.1016/j.comcom.2024.04.024
Kollipara, Y. V. P. (2025). Zero Trust Architecture in Mission-Critical Systems: From Perimeter Security to Continuous Verification. Journal of Information Systems Engineering & Management, 10, 251. https://doi.org/10.52783/jisem.v10i61s.13351
Kommera, S. (2025). Enhancing Zero Trust Architecture with AI-Driven Threat Intelligence in Cloud Environments. International Journal of Scientific Research in Computer Science Engineering and Information Technology, 11(1), 1524. https://doi.org/10.32628/cseit251112163
Kumar, M., Samriya, J. K., Walia, G. K., Verma, P., Wu, H., & Gill, S. S. (2025). Blockchain Empowered Secure Federated Learning for Consumer IoT Applications in Cloud-Edge Collaborative Environment. IEEE Transactions on Consumer Electronics, 71(2), 3986. https://doi.org/10.1109/tce.2025.3532676
Kumar, S., Vahabi, M., & Fotouhi, H. (2025). Federated learning at the edge in Industrial Internet of Things: A review [Review of Federated learning at the edge in Industrial Internet of Things: A review]. Sustainable Computing Informatics and Systems, 46, 101087. Elsevier BV. https://doi.org/10.1016/j.suscom.2025.101087
Kumaravelu, A. (2024). Leveraging AI for Enhanced IT Infrastructure: An Examination of Automated Processes and Their Impacts. International Journal for Research in Applied Science and Engineering Technology, 12(9), 10. https://doi.org/10.22214/ijraset.2024.64036
Li, K., Li, C., Yuan, X., Li, S., Zou, S., Ahmed, S. S., Ni, W., Niyato, D., Jamalipour, A., Dressler, F., & Akan, Ö. B. (2025). Zero-Trust Foundation Models: A New Paradigm for Secure and Collaborative Artificial Intelligence for Internet of Things. IEEE Internet of Things Journal, 1. https://doi.org/10.1109/jiot.2025.3603957
Li, M., Wang, M., & Duan, M. (2025). Atomic and privacy-preserving cyclic cross-chain protocol based on chameleon hash function. Blockchain Research and Applications, 100262. https://doi.org/10.1016/j.bcra.2024.100262
Li, W., Hacid, H., Almazrouei, E., & Debbah, M. (2023). A Comprehensive Review and a Taxonomy of Edge Machine Learning: Requirements, Paradigms, and Techniques [Review of A Comprehensive Review and a Taxonomy of Edge Machine Learning: Requirements, Paradigms, and Techniques]. AI, 4(3), 729. Multidisciplinary Digital Publishing Institute. https://doi.org/10.3390/ai4030039
Lin, H.-T., & Wen, C.-Y. (2024). Edge Federated Optimization for Heterogeneous Data. Future Internet, 16(4), 142. https://doi.org/10.3390/fi16040142
Liu, J., Chen, C., Li, Y., Sun, L., Song, Y., Zhou, J., Jing, B., & Dou, D. (2024a). Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learning. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2403.19178
Liu, J., Chen, C., Li, Y., Sun, L., Song, Y., Zhou, J., Jing, B., & Dou, D. (2024b). Enhancing trust and privacy in distributed networks: a comprehensive survey on blockchain-based federated learning. Knowledge and Information Systems, 66(8), 4377. https://doi.org/10.1007/s10115-024-02117-3
Lo, S. K., Lu, Q., Wang, C., Paik, H.-Y., & Zhu, L. (2021). A Systematic Literature Review on Federated Machine Learning [Review of A Systematic Literature Review on Federated Machine Learning]. ACM Computing Surveys, 54(5), 1. Association for Computing Machinery. https://doi.org/10.1145/3450288
Ma, X., Fang, F., & Wang, X. (2025). Dynamic Authentication and Granularized Authorization with a Cross-Domain Zero Trust Architecture for Federated Learning in Large-Scale IoT Networks. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2501.03601
Madathil, N. T., Dankar, F. K., Gergely, M., Belkacem, A. N., & Alrabaee, S. (2025). Revolutionizing Healthcare Data Analytics with Federated Learning: A Comprehensive Survey of Applications, Systems, and Future Directions [Review of Revolutionizing Healthcare Data Analytics with Federated Learning: A Comprehensive Survey of Applications, Systems, and Future Directions]. Computational and Structural Biotechnology Journal, 28, 217. Elsevier BV. https://doi.org/10.1016/j.csbj.2025.06.009
Mahmood, K., Khan, S., Abdelhaq, M., Hassan, M. ul, Uddin, M., Alsaqour, R., Awan, K. A., & Alsoufi, M. A. (2025). Adaptive resource aware and privacy preserving federated edge learning framework for real time internet of medical things applications. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-23398-w
Marengo, A. (2024). Navigating the nexus of AI and IoT: A comprehensive review of data analytics and privacy paradigms [Review of Navigating the nexus of AI and IoT: A comprehensive review of data analytics and privacy paradigms]. Internet of Things, 27, 101318. Elsevier BV. https://doi.org/10.1016/j.iot.2024.101318
Monteiro, D., Mavinkurve, I., Kambli, P., & Surve, Prof. S. (2024). 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(11), 355. https://doi.org/10.22214/ijraset.2024.65062
Mughal, F. R., He, J., Das, B., Dharejo, F. A., Zhu, N., Bhatia, S., & Alzahrani, S. (2024). Adaptive federated learning for resource-constrained IoT devices through edge intelligence and multi-edge clustering. Scientific Reports, 14(1). https://doi.org/10.1038/s41598-024-78239-z
Munusamy, S., & Jothi, K. (2025a). Blockchain-enabled federated learning with edge analytics for secure and efficient electronic health records management. Scientific Reports, 15(1), 27524. https://doi.org/10.1038/s41598-025-12225-x
Munusamy, S., & Jothi, K. R. (2025b). Blockchain-Enabled Federated Learning with Edge Analytics for Secure and Efficient Electronic Health Records Management. Research Square. https://doi.org/10.21203/rs.3.rs-6678464/v1
Narkedimilli, S., Sriram, A., Makam, S., Sathvik, M., & Mallellu, S. P. (2025). FAPL-DM-BC: A Secure and Scalable FL Framework with Adaptive Privacy and Dynamic Masking, Blockchain, and XAI for the IoVs. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2501.01063
Narkedimilli, S., Sriram, A., & Raghav, S. (2024). FL-DABE-BC: A Privacy-Enhanced, Decentralized Authentication, and Secure Communication for Federated Learning Framework with Decentralized Attribute-Based Encryption and Blockchain for IoT Scenarios. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2410.20259
Nawaz, A., Irfan, M., Yu, X., Zou, Z., & Westerlund, T. (2025). Blockchain-Enabled Privacy-Preserving Second-Order Federated Edge Learning in Personalized Healthcare. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2506.00416
Nori, M. K., Yun, S., & Kim, I. (2021). Fast Federated Learning by Balancing Communication Trade-Offs. IEEE Transactions on Communications, 69(8), 5168. https://doi.org/10.1109/tcomm.2021.3083316
Obuse, E., Ayanbode, N., Cadet, E., Essien, I. A., & Etim, E. D. (2025). Privacy-First security models for AI-integrated identity governance in multi-access cloud and edge environments. Computer Science & IT Research Journal, 6(8), 506. https://doi.org/10.51594/csitrj.v6i8.2012
Ogunbajo, A., Taiwo, I., Abidola, A. Q., Adediran, O. F., & Agbo-Adediran, I. (2025). Privacy preserving AI models for decentralized data management in federated information systems. GSC Advanced Research and Reviews, 22(2), 104. https://doi.org/10.30574/gscarr.2025.22.2.0043
Olagunju, F. (2025). Federated Learning in the Era of Data Privacy: An Exhaustive Survey of Privacy Preserving Techniques, Legal Frameworks, and Ethical Considerations. International Journal of Future Engineering Innovations, 2(3), 153. https://doi.org/10.54660/ijfei.2025.2.3.153-160
Olayinka, O. T., Jeswani, S., & Iloh, D. (2025). Adaptive Cybersecurity Architecture for Digital Product Ecosystems Using Agentic AI. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2509.20640
Otoum, Y., Asad, A., & Nayak, A. (2025). LLMs meet Federated Learning for Scalable and Secure IoT Management. https://doi.org/10.48550/ARXIV.2504.16032
Panda, S. N. (2025). Blockchain Technologies for Secure and Transparent Artificial Intelligence Systems. https://doi.org/10.2139/ssrn.5250372
Paul, É., Paul, É., Kessie, J. D., & Salawudeen, M. D. (2024). Zero trust architecture and AI: A synergistic approach to next-generation cybersecurity frameworks. International Journal of Science and Research Archive, 13(2), 4159. https://doi.org/10.30574/ijsra.2024.13.2.2583
Pokhrel, S. R., Yang, L., Rajasegarar, S., & Li, G. (2024a). Robust Zero Trust Architecture: Joint Blockchain based Federated learning and Anomaly Detection based Framework. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2406.17172
Pokhrel, S. R., Yang, L., Rajasegarar, S., & Li, G. (2024b). Robust Zero Trust Architecture: Joint Blockchain based Federated learning and Anomaly Detection based Framework. 7. https://doi.org/10.1145/3672200.3673878
Poongodi, R. K. (2025). Zero Trust AI Authentication and Blockchain Powered Secure AI. International Journal for Research in Applied Science and Engineering Technology, 13(2), 52. https://doi.org/10.22214/ijraset.2025.66800
Pothireddy, S., Peddisetty, N., Yellamma, P., Botta, G., & Kailash, N. (2024). Data Security in Cloud Environment by Using Hybrid Encryption Technique: A Comprehensive Study on Enhancing Confidentiality and Reliability. International Journal of Intelligent Engineering and Systems, 17(2), 159. https://doi.org/10.22266/ijies2024.0430.14
Puppala, S., Hossain, I., Alam, Md. J., Talukder, S., Ferdaus, J., Hasan, M., Pisupati, S., & Mathukumilli, S. (2024). Generative AI like ChatGPT in Blockchain Federated Learning: use cases, opportunities and future. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2407.18358
Qammar, A., Karim, A., Ning, H., & Ding, J. (2022). Securing federated learning with blockchain: a systematic literature review. Artificial Intelligence Review, 56(5), 3951. https://doi.org/10.1007/s10462-022-10271-9
Rahman, R. (2025). Federated Learning: A Survey on Privacy-Preserving Collaborative Intelligence. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2504.17703
Rahmati, M. (2025). Federated Learning-Driven Cybersecurity Framework for IoT Networks with Privacy-Preserving and Real-Time Threat Detection Capabilities. https://doi.org/10.48550/ARXIV.2502.10599
Ramos, S., & Ellul, J. (2024). Blockchain for Artificial Intelligence (AI): enhancing compliance with the EU AI Act through distributed ledger technology. A cybersecurity perspective. International Cybersecurity Law Review, 5(1), 1. https://doi.org/10.1365/s43439-023-00107-9
Rane, J., Kaya, Ö., Mallick, S. K., & Rane, N. L. (2024). Federated learning for edge artificial intelligence: Enhancing security, robustness, privacy, personalization, and blockchain integration in IoT. https://doi.org/10.70593/978-81-981271-0-5_3
Rangaraju, S. (2023). Secure By Intelligence: Enhancing Products with Ai-Driven Security Measures. EPH - International Journal of Science And Engineering, 9(3), 36. https://doi.org/10.53555/ephijse.v9i3.212
Rauniyar, A., Hagos, D. H., Jha, D., Håkegård, J. E., Bağcı, U., Rawat, D. B., & Vlassov, V. (2023). Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions. IEEE Internet of Things Journal, 11(5), 7374. https://doi.org/10.1109/jiot.2023.3329061
Robai, M. P. (2024). Federated learning for secure and privacy preserving data analytics in heterogeneous networks. GSC Advanced Research and Reviews, 21(2), 527. https://doi.org/10.30574/gscarr.2024.21.2.0451
Sammangi, H., Jagatha, A., Bojja, G. R., & Liu, J. (2025). Decentralized AI-driven IoT Architecture for Privacy-Preserving and Latency-Optimized Healthcare in Pandemic and Critical Care Scenarios. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2507.15859
Samuel-Okon, A. D., Olateju, O. O., Okon, S. U., Olaniyi, O. O., & Igwenagu, U. T. I. (2024). Formulating Global Policies and Strategies for Combating Criminal Use and Abuse of Artificial Intelligence. Archives of Current Research International, 24(5), 612. https://doi.org/10.9734/acri/2024/v24i5735
Sánchez, O. T., Borges, G. A., Raposo, D., Rodrigues, A., Boavida, F., & Silva, J. S. (2025). Federated Learning Framework for LoRaWAN-Enabled IIoT Communication: A Case Study. IEEE Internet of Things Journal, 12(13), 24944. https://doi.org/10.1109/jiot.2025.3558560
Schwermer, R., Mayer, R., & Jacobsen, H. (2024a). Federated Computing -- Survey on Building Blocks, Extensions and Systems. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2404.02779
Schwermer, R., Mayer, R., & Jacobsen, H.-A. (2024b). Federated Computing -- Survey on Building Blocks, Extensions and Systems. https://doi.org/10.48550/ARXIV.2404.02779
Sen, J., Waghela, H., & Rakshit, S. (2024). Privacy in Federated Learning. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2408.08904
Shahsavari, Y., Dambri, O. A., Baseri, Y., Hafid, A., & Makrakis, D. (2024). Integration of Federated Learning and Blockchain in Healthcare: A Tutorial. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2404.10092
Shanmugarasa, Y., Paik, H.-Y., Kanhere, S. S., & Zhu, L. (2023). A systematic review of federated learning from clients’ perspective: challenges and solutions [Review of A systematic review of federated learning from clients’ perspective: challenges and solutions]. Artificial Intelligence Review, 56, 1773. Springer Science+Business Media. https://doi.org/10.1007/s10462-023-10563-8
Shen, S., Zhu, T., Wu, D., Wang, W., & Zhou, W. (2020). From distributed machine learning to federated learning: In the view of data privacy and security. Concurrency and Computation Practice and Experience, 34(16). https://doi.org/10.1002/cpe.6002
Shirvani, G., & Ghasemshirazi, S. (2024). Powering the Future of IoT: Federated Learning for Optimized Power Consumption and Enhanced Privacy. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2405.03065
Short, J. E., Miyachi, K., Toouli, C., & Todd, S. (2022). A field test of a federated learning/federated analytic blockchain network implementation in an HPC environment. Frontiers in Blockchain, 5. https://doi.org/10.3389/fbloc.2022.893747
Singh, J., Bharany, S., Rani, S., Rehman, A. U., Taye, B. M., Pant, R., & Kaur, U. (2025). A Systematic Review of Blockchain, AI, and Cloud Integration for Secure Digital Ecosystems. The International Journal of Networked and Distributed Computing, 13(2). https://doi.org/10.1007/s44227-025-00072-1
Stephan, T., Paramana, P. P. D., Lin, C., Agarwal, S., & Verma, R. (2025). Federated learning-driven IoT system for automated freshness monitoring in resource-constrained vending carts. Journal of Big Data, 12(1). https://doi.org/10.1186/s40537-025-01063-3
Sumanth, N., Heggere, S. B., Prathiksha, E., Shetty, C. A., Nikhitha, H. R., & Soundarya, B. C. (2023). Federated Learning: Healthcare, Security, Challenges, and Threats. Research Square. https://doi.org/10.21203/rs.3.rs-3223963/v1
Sun, N. X., Miao, Y., Jiang, H., Ding, M. D., & Zhang, J. (2024). From Principles to Practice: A Deep Dive into AI Ethics and Regulations. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2412.04683
Tang, Y., Wang, K., Niyato, D., Chen, W., & Karagiannidis, G. K. (2024). Digital Twin-Assisted Federated Learning with Blockchain in Multi-tier Computing Systems. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2411.02323
Thomas, S. G., & Myakala, P. K. (2025). Beyond the Cloud: Federated Learning and Edge AI for the Next Decade. Journal of Computer and Communications, 13(2), 37. https://doi.org/10.4236/jcc.2025.132004
Uddin, M. R., Shankar, G., Mukta, S. H., Kumar, P., & Islam, A. K. M. N. (2024). Evolving Topics in Federated Learning: Trends, and Emerging Directions for IS. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2409.15773
Uddoh, J., Ajiga, D., Okare, B. P., & Aduloju, T. D. (2021). AI-Based Threat Detection Systems for Cloud Infrastructure: Architecture, Challenges, and Opportunities. Journal of Frontiers in Multidisciplinary Research, 2(2), 61. https://doi.org/10.54660/.ijfmr.2021.2.2.61-67
Vermesan, O., Eisenhauer, M., Sundmaeker, H., Guillemin, P., Serrano, M., Τράγος, Η., Valiño, J., derWees, A. van, Gluhak, A., & Bahr, R. (2022). Internet of Things Cognitive Transformation Technology Research Trends and Applications. In River Publishers eBooks (p. 17). https://doi.org/10.1201/9781003337584-3
Vyhmeister, E., & Castañé, G. G. (2024). When Industry meets Trustworthy AI: A Systematic Review of AI for Industry 5.0 [Review of When Industry meets Trustworthy AI: A Systematic Review of AI for Industry 5.0]. arXiv (Cornell University). Cornell University. https://doi.org/10.48550/arxiv.2403.03061
Wang, Y., Li, Y., & Chang, X. (2025). AI Scaling: From Up to Down and Out. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2502.01677
Wang, Z., Sun, R., Lui, E., Shah, V. H., Xiong, X., Sun, J., Crapis, D., & Knottenbelt, W. J. (2024). SoK: Decentralized AI (DeAI). arXiv (Cornell University). https://doi.org/10.48550/arxiv.2411.17461
Wu, Z., Sun, S., Wang, Y., Liu, M., Xu, K., Pan, Q., Gao, B., & Tian, W. (2025). Beyond Model Scale Limits: End-Edge-Cloud Federated Learning with Self-Rectified Knowledge Agglomeration. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2501.00693
Yalla, M. R. (2024). Zero-trust security architecture in the ai era: a novel framework for enterprise cyber resilience. International Journal of Science and Research Archive, 13(2), 4341. https://doi.org/10.30574/ijsra.2024.13.2.0172
Yang, H. W., Liu, H., Yuan, X., Wu, K., Ni, W., Zhang, J. A., & Liu, R. P. (2025). Synergizing Intelligence and Privacy: A Review of Integrating Internet of Things, Large Language Models, and Federated Learning in Advanced Networked Systems [Review of Synergizing Intelligence and Privacy: A Review of Integrating Internet of Things, Large Language Models, and Federated Learning in Advanced Networked Systems]. Applied Sciences, 15(12), 6587. Multidisciplinary Digital Publishing Institute. https://doi.org/10.3390/app15126587
Zhang, T., Gao, L., He, C., Zhang, M., Krishnamachari, B., & Avestimehr, A. S. (2022a). Federated Learning for the Internet of Things: Applications, Challenges, and Opportunities. IEEE Internet of Things Magazine, 5(1), 24. https://doi.org/10.1109/iotm.004.2100182
Zhang, T., Gao, L., He, C., Zhang, M., Krishnamachari, B., & Avestimehr, S. (2022b). Federated Learning for Internet of Things: Applications, Challenges, and Opportunities. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2111.07494
Zhang, T., He, C., Ma, T., Gao, L., Ma, M., & Avestimehr, S. (2021). Federated Learning for Internet of Things. 413. https://doi.org/10.1145/3485730.3493444
Zhao, J., Bagchi, S., Avestimehr, A. S., Chan, K., Chaterji, S., Dimitriadis, D., Li, J., Li, N., Nourian, A., & Roth, H. R. (2025). The Federation Strikes Back: A Survey of Federated Learning Privacy Attacks, Defenses, Applications, and Policy Landscape [Review of The Federation Strikes Back: A Survey of Federated Learning Privacy Attacks, Defenses, Applications, and Policy Landscape]. ACM Computing Surveys, 57(9), 1. Association for Computing Machinery. https://doi.org/10.1145/3724113
Zhu, H., Huang, L., & Xie, Z. (2024). Privacy Attack in Federated Learning is Not Easy: An Experimental Study. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2409.19301
Zou, Y. L., Kuek, F., Feng, W., & Cheng, X. (2025). Digital learning in the 21st century: trends, challenges, and innovations in technology integration. Frontiers in Education, 10. https://doi.org/10.3389/feduc.2025.1562391
Zuo, X., Wang, M., Zhu, T., Zhang, L., Ye, D., Yu, S., & Zhou, W. (2024). Federated TrustChain: Blockchain-Enhanced LLM Training and Unlearning. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2406.04076
Zuo, X., Wang, M., Zhu, T., Zhang, L., Yu, S., & Zhou, W. (2024). Federated Learning with Blockchain-Enhanced Machine Unlearning: A Trustworthy Approach. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2405.20776
Zuo, X., Wang, M., Zhu, T., Zhang, L., Yu, S., & Zhou, W. (2025). Federated Learning with Blockchain-Enhanced Machine Unlearning: A Trustworthy Approach. IEEE Transactions on Services Computing, 1. https://doi.org/10.1109/tsc.2025.3553709
