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How to cite this paper
APA: Amiri, B. (2025). A scientometric analysis of the convergence of distributed machine learning, federated learning, and privacy-preserving technologies (2020-2024). Scientometrica, 1(4), 143-152.
Chicago/Turabian: Amiri, B. 2025. "A scientometric analysis of the convergence of distributed machine learning, federated learning, and privacy-preserving technologies (2020-2024)." Scientometrica 1, no. 4 (2025): 143-152.
AMA: Amiri, B. A scientometric analysis of the convergence of distributed machine learning, federated learning, and privacy-preserving technologies (2020-2024). Scientometrica. 2025;1(4):143-152.
References
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Aziz, R., Banerjee, S., Bouzefrane, S., & Le Vinh, T. (2023). Exploring Homomorphic Encryption and Differential Pri-vacy Techniques towards Secure Federated Learning Paradigm. Future Internet, 15(9), 310.
Beitollahi, M., & Lu, N. (2022). FLAC: Federated Learning with Autoencoder Compression and Convergence Guaran-tee. Proceedings - IEEE Global Communications Conference, GLOBECOM, 4589–4594.
Boobalan, P., Ramu, S. P., Pham, Q.-V., Dev, K., Pandya, S., Maddikunta, P. K. R., Gadekallu, T. R., & Huynh-The, T. (2022). Fusion of Federated Learning and Industrial Internet of Things: A survey. Computer Networks, 212, 109048.
Briggs, C., Fan, Z., & Andras, P. (2021). A Review of Privacy-Preserving Federated Learning for the Internet-of-Things. Studies in Computational Intelligence, 965, 21–50.
Chamikara, M. A. P., Bertok, P., Khalil, I., Liu, D., & Camtepe, S. (2021). Privacy preserving distributed machine learn-ing with federated learning. Computer Communications, 171, 112–125.
Chang, Y., Zhang, K., Gong, J., & Qian, H. (2023). Privacy-Preserving Federated Learning via Functional Encryption, Revisited. IEEE Transactions on Information Forensics and Security, 18, 1855–1869.
Chen, Q., Wang, Z., Chen, J., Yan, H., & Lin, X. (2023). Dap-FL: Federated Learning Flourishes by Adaptive Tuning and Secure Aggregation. IEEE Transactions on Parallel and Distributed Systems, 34(6), 1923–1941.
Chen, X., Yu, H., Jia, X., & Yu, X. (2023). APFed: Anti-Poisoning Attacks in Privacy-Preserving Heterogeneous Feder-ated Learning. IEEE Transactions on Information Forensics and Security, 18, 5749–5761.
Chen, Z., Zhang, H., Li, X., Miao, Y., Zhang, X., Zhang, M., Ma, S., & Deng, R. H. (2024). FDFL: Fair and Discrepancy-Aware Incentive Mechanism for Federated Learning. IEEE Transactions on Information Forensics and Security, 19, 8140–8154.
Dai, M., Xu, A., Huang, Q., Zhang, Z., & Lin, X. (2021). Vertical federated DNN training. Physical Communication, 49, 101465.
Deist, T. M., Dankers, F. J. W. M., Ojha, P., Scott Marshall, M., Janssen, T., Faivre-Finn, C., Masciocchi, C., Valentini, V., Wang, J., Chen, J., Zhang, Z., Spezi, E., Button, M., Jan Nuyttens, J., Vernhout, R., van Soest, J., Jochems, A., Monshouwer, R., Bussink, J., … Dekker, A. (2020). Distributed learning on 20 000+ lung cancer patients – The Per-sonal Health Train. Radiotherapy and Oncology, 144, 189–200.
Deng, W., Chen, X., Li, X., & Zhao, H. (2024). Adaptive Federated Learning With Negative Inner Product Aggrega-tion. IEEE Internet of Things Journal, 11(4), 6570–6581.
Elgabli, A., Park, J., Issaid, C. B., & Bennis, M. (2021). Harnessing Wireless Channels for Scalable and Privacy-Preserving Federated Learning. IEEE Transactions on Communications, 69(8), 5194–5208.
Fereidooni, H., Marchal, S., Miettinen, M., Mirhoseini, A., Mollering, H., Nguyen, T. D., Rieger, P., Sadeghi, A.-R., Schneider, T., Yalame, H., & Zeitouni, S. (2021). SAFELearn: Secure Aggregation for private FEderated Learn-ing. *Proceedings - 2021 IEEE Symposium on Security and Privacy Workshops, SPW 2021*, 56–62.
Fisichella, M., & Lax, G. (2022). Partially-federated learning: A new approach to achieving privacy and effective-ness. Information Sciences, 614, 534–547.
Gao, S., Luo, J., Zhu, J., Dong, X., & Shi, W. (2023). VCD-FL: Verifiable, Collusion-Resistant, and Dynamic Federated Learning. IEEE Transactions on Information Forensics and Security, 18, 3760–3773.
Ge, L., Li, H., Wang, X., & Wang, Z. (2023). A review of secure federated learning: Privacy leakage threats, protection technologies, challenges and future directions. Neurocomputing, 561, 126897.
Grataloup, A., Jonas, S., & Meyer, A. (2024). A review of federated learning in renewable energy applications: Poten-tial, challenges, and future directions. Energy and AI, 17, 100375.
Guo, P., Yang, D., Hatamizadeh, A., Xu, A., Xu, Z., Li, W., Zhao, C., Xu, D., Harmon, S., Turkbey, E., Turkbey, B., Wood, B., Patella, F., Stellato, E., Carrafiello, G., Patel, V. M., & Roth, H. R. (2022). Auto-FedRL: Federated Hy-perparameter Optimization for Multi-institutional Medical Image Segmentation. Lecture Notes in Computer Science, 13681 LNCS, 437–455.
Hahn, C., Kim, H., Kim, M., & Hur, J. (2023). VerSA: Verifiable Secure Aggregation for Cross-Device Federated Learning. IEEE Transactions on Dependable and Secure Computing, 20(1), 36–52.
Han, Q., Lu, S., Wang, W., Qu, H., Li, J., & Gao, Y. (2024). Privacy preserving and secure robust federated learning: A survey. Concurrency and Computation: Practice and Experience, 36(13), e8084.
Huang, X., Wu, Y., Liang, C., Chen, Q., & Zhang, J. (2023). Distance-Aware Hierarchical Federated Learning in Block-chain-Enabled Edge Computing Network. IEEE Internet of Things Journal, 10(21), 19163–19176.
Jiang, C., Xu, C., & Zhang, Y. (2021). PFLM: Privacy-preserving federated learning with membership proof. Information Sciences, 576, 288–311.
Jin, R., Hu, J., Min, G., & Mills, J. (2023). Lightweight Blockchain-Empowered Secure and Efficient Federated Edge Learning. IEEE Transactions on Computers, 72(11), 3314–3325.
Kang, Y., & Li, B. (2024). Polaris: Accelerating Asynchronous Federated Learning with Client Selection. IEEE Trans-actions on Cloud Computing, 12(2), 446–458.
Ku, H., Susilo, W., Zhang, Y., Liu, W., & Zhang, M. (2022). Privacy-Preserving federated learning in medical diagnosis with homomorphic re-Encryption. Computer Standards and Interfaces, 80, 103583.
Li, D., Han, D., Weng, T.-H., Zheng, Z., Li, H., Liu, H., Castiglione, A., & Li, K.-C. (2022). Blockchain for federated learning toward secure distributed machine learning systems: a systemic survey. Soft Computing, 26(9), 4423–4440.
Li, Y., Wang, X., Zeng, R., Yang, M., Li, K., Huang, M., & Dustdar, S. (2023). VARF: An Incentive Mechanism of Cross-Silo Federated Learning in MEC. IEEE Internet of Things Journal, 10(17), 15115–15132.
Lim, W. Y. B., Ng, J. S., Xiong, Z., Niyato, D., Miao, C., & Kim, D. I. (2021). Dynamic Edge Association and Resource Allocation in Self-Organizing Hierarchical Federated Learning Networks. IEEE Journal on Selected Areas in Com-munications, 39(12), 3640–3653.
Lin, L., & Zhang, X. (2023). PPVerifier: A Privacy-Preserving and Verifiable Federated Learning Method in Cloud-Edge Collaborative Computing Environment. IEEE Internet of Things Journal, 10(10), 8878–8892.
Liu, X., Li, H., Xu, G., Chen, Z., Huang, X., & Lu, R. (2021). Privacy-Enhanced Federated Learning against Poisoning Adversaries. IEEE Transactions on Information Forensics and Security, 16, 4574–4588.
Liu, Y.-X., Chen, H., Liu, Y.-H., & Li, C.-P. (2022). Privacy-preserving Techniques in Federated Learning. Ruan Jian Xue Bao/Journal of Software, 33(3), 1057–1092.
Mahawaga Arachchige, P. C., Liu, D., Camtepe, S., Nepal, S., Grobler, M., Bertok, P., & Khalil, I. (2022). Local Differ-ential Privacy for Federated Learning. Lecture Notes in Computer Science, 13554 LNCS, 195–216.
Makkar, A., & Santosh, K. (2023). SecureFed: federated learning empowered medical imaging technique to analyze lung abnormalities in chest X-rays. International Journal of Machine Learning and Cybernetics, 14(8), 2659–2670.
Matschinske, J., Späth, J., Bakhtiari, M., Probul, N., Majdabadi, M. M. K., Nasirigerdeh, R., Torkzadehmahani, R., Hartebrodt, A., Orban, B.-A., Fejér, S.-J., Zolotareva, O., Das, S., Baumbach, L., Pauling, J. K., Tomašević, O., Biha-ri, B., Bloice, M., Donner, N. C., Fdhila, W., … Baumbach, J. (2023). The FeatureCloud Platform for Federated Learning in Biomedicine: Unified Approach. Journal of Medical Internet Research, 25, e42621.
Miao, Y., Kuang, D., Li, X., Xu, S., Li, H., Raymond Choo, K.-K., & Deng, R. H. (2024). Privacy-Preserving Asynchro-nous Federated Learning Under Non-IID Settings. IEEE Transactions on Information Forensics and Security, 19, 5828–5841.
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