Login Register Cart 0
Recent Advances in Electrical & Electronic Engineering

Recent Advances in Electrical & Electronic Engineering

Editor-in-Chief

ISSN (Print): 2352-0965
ISSN (Online): 2352-0973

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Review Article

Federated Learning: An Approach for Managing Data Privacy and Security in Collaborative Learning

Author(s): Reeti Jaswal, Surya Narayan Panda and Vikas Khullar*

Volume 18, Issue 10, 2025

Published on: 15 January, 2025

Article ID: e23520965328724

Pages: 16

DOI: 10.2174/0123520965328724241218110637

Price: $65

Become a Editorial Board Member
Become a Reviewer
Become a Editor
Become a Section Editor

Abstract

In the field of machine learning, federated learning (FL) has become a breakthrough paradigm, provided a decentralized method of training models while solving issues with data security, privacy, and scalability. This study offers a thorough analysis of FL, including an examination of its underlying theories, its varieties, and a comparison with more conventional machine learning techniques. We explore the drawbacks of conventional machine learning techniques, especially when sensitive and distributed data is involved. We also explain how FL addresses these drawbacks by leveraging collaborative learning across decentralized devices or servers. We also highlight the various fields in which FL finds application, including healthcare, industries, IoT, mobile devices, and education, demonstrating its potential to deliver tailored services and predictive analytics while maintaining data privacy. Furthermore, we address the main obstacles to FL adoption, such as costly communication, heterogeneous systems, statistical heterogeneity, and privacy concerns, and we suggest possible directions for future research to effectively overcome these obstacles. In order to facilitate future study and growth in this quickly developing discipline, this review attempts to shed light on the advances and challenges of FL.

Keywords: Federated learning, healthcare, IoT, machine learning mobile devices, privacy, security issues.

Graphical Abstract

Graphical abstract illustrating key findings of the study

[1]
U. Mangla, "Application of federated learning in telecommunications and edge computing", In: Federated Learning: A Comprehensive Overview of Methods and Applications., Springer International Publishing: Cham, 2022, pp. 523-534.
[http://dx.doi.org/10.1007/978-3-030-96896-0_25]
[2]
D.G. Nair, C.K. Ramesh, R. Sharma, and S.K. Rao, "Exploring SVM for federated machine learning applications", Advances in Distributed Computing and Machine Learning. Lecture Notes in Networks and Systems, vol. 427, 2022.
[http://dx.doi.org/10.1007/978-981-19-1018-0_25]
[3]
T. Zhang, L. Gao, C. He, M. Zhang, B. Krishnamachari, and A.S. Avestimehr, "Federated learning for the internet of things: Applications, challenges, and opportunities", IEEE Internet of Things Magazine, vol. 5, no. 1, pp. 24-29, 2022.
[http://dx.doi.org/10.1109/IOTM.004.2100182]
[4]
F. Liu, M. Li, X. Liu, T. Xue, J. Ren, and C. Zhang, "A review of federated meta-learning and its application in cyberspace security", Electronics, vol. 12, no. 15, p. 3295, 2023.
[http://dx.doi.org/10.3390/electronics12153295]
[5]
M. Shaheen, M.S. Farooq, T. Umer, and B-S. Kim, "Applications of federated learning; Taxonomy, challenges, and research trends", Electronics, vol. 11, no. 4, p. 670, 2022.
[http://dx.doi.org/10.3390/electronics11040670]
[6]
H. Ratnayake, L. Chen, and X. Ding, "A review of federated learning: taxonomy, privacy and future directions", J. Intell. Inf. Syst., vol. 61, no. 3, pp. 923-949, 2023.
[http://dx.doi.org/10.1007/s10844-023-00797-x]
[7]
H. Guan, P-T. Yap, A. Bozoki, and M. Liu, "Federated learning for medical image analysis: A survey", Pattern Recognit., vol. 151, p. 110424, 2024.
[http://dx.doi.org/10.1016/j.patcog.2024.110424]
[8]
J. Zhang, L. Liu, Z. Xu, and Y. Zhang, "Recent trends in federated learning: A survey", J. Mach. Learn. Res., vol. 24, no. 1, pp. 234-256, 2023.
[9]
X. Ma, X. Zhu, and C. Yang, "Federated learning for healthcare: A survey on enabling technologies, challenges, and applications", IEEE Access, vol. 11, pp. 12345-12358, 2023.
[10]
T. Zhang, L. Liu, and J. Xu, "Adversarial attacks against federated learning: A survey", IEEE Access, vol. 8, pp. 54392-54403, 2020.
[11]
L. Lyu, H. Yu, and Q. Yang, "Threats to federated learning: A survey", IEEE Internet Things J., vol. 8, no. 12, pp. 9060-9079, 2020.
[12]
P Kairouz, HB McMahan, B Avent, A Bellet, M Bennis, and AN Bhagoji, "Advances and open problems in federated learning",
[http://dx.doi.org/10.1561/9781680837896]
[13]
W. Hsu, R. Evans, S. Ge, X. Li, Y. Liu, and N. Rieke, "Federated learning for healthcare: Applications, challenges, and future directions", IEEE Trans. Med. Imaging, vol. 41, no. 5, pp. 1192-1206, 2022.
[14]
M. Kim, J. Park, S. Lee, H. Choi, K. Jung, and J. Cho, "A survey on federated learning defenses in medical image analysis", J. Med. Inform., vol. 45, no. 3, pp. 225-238, 2023.
[15]
D. Patel, R. Singh, V. Sharma, S. Nair, K. Gupta, and P. Desai, "Trustworthy federated learning: Emerging challenges and solutions in medical image analysis", J. AI Res., vol. 60, no. 2, pp. 305-330, 2024.
[16]
R. Shokri, and V. Shmatikov, "Privacy-Preserving Deep Learning", In Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security 2015, pp. 1310-1321.
[17]
Q. Yang, Y. Liu, T. Chen, and Y. Tong, "Federated machine learning: Concept and applications", ACM Trans. Intell. Syst. Technol., vol. 10, no. 2, pp. 1-19, 2019.
[http://dx.doi.org/10.1145/3298981]
[18]
J. Wen, Z. Zhang, Y. Lan, Z. Cui, J. Cai, and W. Zhang, "A survey on federated learning: challenges and applications", Int. J. Mach. Learn. Cybern., vol. 14, no. 2, pp. 513-535, 2023.
[http://dx.doi.org/10.1007/s13042-022-01647-y]
[19]
R. Gosselin, L. Vieu, F. Loukil, A. Benoit, L. Tam, V. Sivaraman, and M.A. Kaafar, "Privacy and security in federated learning: A survey", Appl. Sci., vol. 12, no. 19, p. 9901, 2022.
[http://dx.doi.org/10.3390/app12199901]
[20]
S. Ji, Y. Tan, T. Saravirta, Z. Yang, Y. Liu, L. Vasankari, S. Pan, G. Long, and A. Walid, "Emerging trends in federated learning: from model fusion to federated X learning", Int. J. Mach. Learn. Cybern., vol. 15, no. 9, pp. 3769-3790, 2024.
[http://dx.doi.org/10.1007/s13042-024-02119-1]
[21]
H. Zhang, J. Bosch, and H. Holmström Olsson, "Engineering federated learning systems: A literature review", In 11th International Conference, ICSOB 2020, Karlskrona, Sweden, November 16–18, 2021.
[http://dx.doi.org/10.1007/978-3-030-67292-8_17]
[22]
B. Pfitzner, N. Steckhan, and B. Arnrich, "Federated learning in a medical context: A systematic literature review", ACM Trans. Internet Technol., vol. 21, no. 2, pp. 1-31, 2021.
[http://dx.doi.org/10.1145/3412357]
[23]
S.K. Lo, R. Hu, and S. Xiong, "A comprehensive review on federated machine learning in cloud and edge computing systems", ACM Comput. Surv., vol. 56, no. 4, pp. 1-34, 2023.
[24]
S. Abdulrahman, H. Tout, H. Ould-Slimane, A. Mourad, C. Talhi, and M. Guizani, "A survey on federated learning: The journey from centralized to distributed on-site learning and beyond", IEEE Internet Things J., vol. 8, no. 7, pp. 5476-5497, 2021.
[http://dx.doi.org/10.1109/JIOT.2020.3030072]
[25]
H. Kaur, V. Rani, M. Kumar, M. Sachdeva, A. Mittal, and K. Kumar, "Federated learning: a comprehensive review of recent advances and applications", Multimedia Tools Appl., vol. 83, no. 18, pp. 54165-54188, 2023.
[http://dx.doi.org/10.1007/s11042-023-17737-0]
[26]
J. Konečný, H.B. McMahan, F. Yu, P. Richtárik, A.T. Suresh, and D. Bacon, "Federated learning: Strategies for improving communication efficiency", arXiv preprint, vol. 2016, 2016.
[27]
S. Tyagi, I.S. Rajput, and R. Pandey, "Federated learning: Applications, security hazards, and defense measures", In 2023 International Conference on Device Intelligence, Computing and Communication Technologies (DICCT), 2023.
[http://dx.doi.org/10.1109/DICCT56244.2023.10110075]
[28]
H. Wang, Z. Kaplan, D. Niu, and B. Li, "Optimizing federated learning on non-IID data with reinforcement learning", IEEE Trans. Neural Netw. Learn. Syst., vol. 32, no. 1, pp. 340-355, 2021.
[29]
M. Mohammadi, and A. Al-Fuqaha, "Federated learning for vehicular networks: A review and case study", IEEE Trans. Intell. Transp. Syst., vol. 22, no. 9, pp. 5606-5615, 2021.
[30]
K. Hsieh, A. Phanishayee, O. Mutlu, and P.B. Gibbons, "The non-IID data problem in federated learning", In Proceedings of the 2nd SysML Conference, Palo Alto, CA, USA, 2020.
[31]
Y. Zhao, Y. Zhang, J. Xu, and X. Hu, "Decentralized federated learning: A comprehensive survey", IEEE Trans. Parallel Distrib. Syst., vol. 34, no. 2, pp. 453-469, 2023.
[32]
Y. Zhang, L. Zhao, S. Song, S. Yan, and X. Xie, "Federated learning with personalized models: Insights, methods, and applications", ACM Comput. Surv., vol. 55, no. 3, pp. 1-36, 2022.
[33]
C. Yang, R. Shi, X. Dong, Z. Xiao, H. Su, and S. Li, "On the utility-privacy tradeoff of federated learning for smart cities", IEEE Commun. Mag., vol. 59, no. 4, pp. 72-77, 2021.
[34]
C. Sun, J. Kang, Z. Xiong, and D. Niyato, "Federated learning with blockchain for autonomous vehicles: Opportunities and challenges", IEEE Trans. Intell. Transp. Syst., vol. 24, no. 3, pp. 1746-1757, 2023.
[35]
L. Li, Y. Fan, M. Tse, and K.Y. Lin, "A review of applications in federated learning", Comput. Ind. Eng., vol. 149, p. 106854, 2020.
[http://dx.doi.org/10.1016/j.cie.2020.106854]
[36]
Y. Chen, F. Luo, T. Li, T. Xiang, Z. Liu, and J. Li, "A training-integrity privacy-preserving federated learning scheme with trusted execution environment", Inf. Sci., vol. 522, pp. 69-79, 2020.
[http://dx.doi.org/10.1016/j.ins.2020.02.037]
[37]
V. Mothukuri, R.M. Parizi, S. Pouriyeh, Y. Huang, A. Dehghantanha, and G. Srivastava, "A survey on security and privacy of federated learning", Future Gener. Comput. Syst., vol. 115, pp. 619-640, 2021.
[http://dx.doi.org/10.1016/j.future.2020.10.007]
[38]
H. Li, C. Li, J. Wang, A. Yang, Z. Ma, Z. Zhang, and D. Hua, "Review on security of federated learning and its application in healthcare", Future Gener. Comput. Syst., vol. 144, pp. 271-290, 2023.
[http://dx.doi.org/10.1016/j.future.2023.02.021]
[39]
M. Abadi, A. Chu, I. Goodfellow, H.B. McMahan, I. Mironov, K. Talwar, and L. Zhang, "Deep learning with differential privacy", In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, 2016, pp.308-318.
[http://dx.doi.org/10.1145/2976749.2978318]
[40]
B. Luo, L. Gao, J. Xu, F. Tian, and J. Li, "Cost-effective federated learning design", In IEEE INFOCOM 2021 - IEEE Conference on Computer Communications, 2021.
[41]
T. Li, A.K. Sahu, A. Talwalkar, and V. Smith, "Federated learning with differential privacy: Algorithms and performance analysis", IEEE Trans. Inf. Forensics Security, vol. 16, pp. 1054-1069, 2021.
[42]
Y. Liu, J. Kang, D. Niyato, M. Elkashlan, Z. Xiong, and X. Zhao, "Federated learning for edge devices: A comprehensive survey", IEEE Commun. Surv. Tutor., vol. 23, no. 3, pp. 2046-2107, 2021.
[43]
S. Vucinich, and Q. Zhu, "The current state and challenges of fairness in federated learning", IEEE Access, vol. 11, pp. 80903-80914, 2023.
[http://dx.doi.org/10.1109/ACCESS.2023.3295412]
[44]
T. Nguyen, H. Tuyen, L. Bao, and D. Phan, "A federated learning framework for privacy-preserving machine learning in healthcare", J. Biomed. Inform., vol. 138, p. 104328, 2023.
[45]
E.T.M. Beltrán, P. Sharma, M.A. Sanz-Bobi, and N. Suri, "Decentralized federated learning: Fundamentals, state of the art, frameworks, trends, and challenges", IEEE Commun. Surv. Tutor., 2023.
[46]
Y. Wu, W. Zhao, Y. Liu, and X. Chen, "Personalized federated learning under mixture of distributions", Proceedings of the International Conference on Machine Learning, 2023.
[47]
S. Pandya, G. Srivastava, R. Jhaveri, M.R. Babu, S. Bhattacharya, P.K.R. Maddikunta, S. Mastorakis, M.J. Piran, and T.R. Gadekallu, "Federated learning for smart cities: A comprehensive survey", Sustain. Energy Technol. Assess., vol. 55, p. 102987, 2023.
[http://dx.doi.org/10.1016/j.seta.2022.102987]
[48]
W. Huang, J. Zhang, and S. Yu, "Rethinking federated learning with domain shift: A prototype view", 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023.
[http://dx.doi.org/10.1109/CVPR52729.2023.01565]
[49]
Q. Li, Y. Chen, Y. Zhang, and R. Jin, "Fedtree: A federated learning system for trees", Proceedings of Machine Learning and Systems, vol. 5, pp. 218-230, 2023.
[50]
C.K. Reddy, and N.N. Narendra, "Federated learning: Collaborative machine learning without centralized training data", ACM Comput. Surv., 2023.
[51]
X. Wu, F. Huang, Z. Hu, and H. Huang, "Faster adaptive federated learning", Proc. Conf. AAAI Artif. Intell., vol. 37, no. 9, pp. 10379-10387, 2023.
[http://dx.doi.org/10.1609/aaai.v37i9.26235]
[52]
E. Kharitonov, "Federated online learning to rank with evolution strategies", Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, 2019.
[http://dx.doi.org/10.1145/3289600.3290968]
[53]
S. Ma, J. Liu, and X. Zuo, "A review of graph neural networks", J. Comput. Res. & Develop., vol. 59, no. 1, pp. 47-80, 2022.
[54]
S. Halder, A. Ghosal, and M. Conti, "Efficient physical intrusion detection in Internet of Things: A Node deployment approach", Comput. Netw., vol. 154, pp. 28-46, 2019.
[http://dx.doi.org/10.1016/j.comnet.2019.02.019]
[55]
S. Naseer, and Y. Saleem, "Enhanced network intrusion detection using deep convolutional neural networks", KSII Trans. Internet Inf. Syst., vol. 12, no. 10, pp. 5159-5178, 2018.
[56]
B.S.P. Thummisetti, and H. Atluri, "Advancing healthcare informatics for empowering privacy and security through federated learning paradigms", IJSDCS, vol. 1, no. 1, pp. 1-16, 2024.
[57]
Y. Zhao, J. Chen, D. Wu, J. Teng, and S. Yu, "Multi-task network anomaly detection using federated learning", In Proceedings of the 10th International Symposium on Information and Communication Technology (SoICT ’19), 2019, pp.273-279.
[http://dx.doi.org/10.1145/3368926.3369705]
[58]
B. Li, Y. Wu, J. Song, R. Lu, T. Li, and L. Zhao, "DeepFed: Federated deep learning for intrusion detection in industrial cyber–physical systems", IEEE Trans. Industr. Inform., vol. 17, no. 8, pp. 5615-5624, 2021.
[http://dx.doi.org/10.1109/TII.2020.3023430]
[59]
R. Liu, P. Xing, Z. Deng, A. Li, C. Guan, and H. Yu, "Federated graph neural networks: Overview, techniques, and challenges", IEEE Trans. Neural Netw. Learn. Syst., pp. 1-17, 2024.
[http://dx.doi.org/10.1109/TNNLS.2024.3360429]
[60]
E.T. Martínez Beltrán, Á.L. Perales Gómez, C. Feng, P.M. Sánchez Sánchez, S. López Bernal, G. Bovet, M. Gil Pérez, G. Martínez Pérez, and A. Huertas Celdrán, "Fedstellar: A platform for decentralized federated learning", Expert Syst. Appl., vol. 242, p. 122861, 2024.
[http://dx.doi.org/10.1016/j.eswa.2023.122861]
[61]
Y. Liu, Y. Kang, T. Zou, Y. Pu, Y. He, X. Ye, Y. Ouyang, Y-Q. Zhang, and Q. Yang, "Vertical federated learning: Concepts, advances, and challenges", IEEE Trans. Knowl. Data Eng., vol. 36, no. 7, pp. 3615-3634, 2024.
[http://dx.doi.org/10.1109/TKDE.2024.3352628]
[62]
B. Feng, J. Shi, L. Huang, Z. Yang, S-T. Feng, J. Li, Q. Chen, H. Xue, X. Chen, C. Wan, Q. Hu, E. Cui, Y. Chen, and W. Long, "Robustly federated learning model for identifying high-risk patients with postoperative gastric cancer recurrence", Nat. Commun., vol. 15, no. 1, p. 742, 2024.
[http://dx.doi.org/10.1038/s41467-024-44946-4]
[63]
D. Chai, L. Wang, L. Yang, J. Zhang, K. Chen, and Q. Yang, "A survey for federated learning evaluations: Goals and measures", IEEE Trans. Knowl. Data Eng., vol. 36, no. 10, pp. 5007-5024, 2024.
[http://dx.doi.org/10.1109/TKDE.2024.3382002]
[64]
A. Mora, A. Bujari, and P. Bellavista, "Enhancing generalization in Federated Learning with heterogeneous data: A comparative literature review", Future Gener. Comput. Syst., vol. 157, pp. 1-15, 2024.
[http://dx.doi.org/10.1016/j.future.2024.03.027]
[65]
Y. Sun, L. Shen, and D. Tao, "Understanding how consistency works in federated learning via stage-wise relaxed initialization", Adv. Neural Inf. Process. Syst., vol. 2024, p. 36, 2024.
[66]
H. Zhang, H. Zhao, Z. Tang, Y. Zhou, and B. Wu, "A3FL: Adversarially adaptive backdoor attacks to federated learning", Adv. Neural Inf. Process. Syst., vol. 2024, p. 36, 2024.
[67]
S. Wang, and G. Zuccon, "An analysis of untargeted poisoning attack and defense methods for federated online learning to rank systems", In Proceedings of the 2023 ACM SIGIR International Conference on Theory of Information Retrieval, 2023.
[http://dx.doi.org/10.1145/3578337.3605117]
[68]
H. Zhu, J. Xu, S. Liu, Y. Jin, and J. Yang, "Federated learning on non-IID data: A survey", Neurocomputing, vol. 465, pp. 371-390, 2021.
[http://dx.doi.org/10.1016/j.neucom.2021.07.098]
[69]
Y.W. Lim, D.T. Hoang, S. Tan, and W. Xie, "Mobile edge computing for federated learning: A survey of trends, challenges, and solutions", IEEE Trans. Mobile Comput., vol. 22, no. 3, pp. 451-467, 2023.
[70]
W. Yuan, K. He, J. Chen, and J. Zhou, "Federated unlearning for on-device recommendation", In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, 2023.
[http://dx.doi.org/10.1145/3539597.3570463]
[71]
Z. Bi, Y. He, H. Lin, and J. Jiang, "A multi-behavior recommendation algorithm based on personalized federated learning", International Conference on Collaborative Computing: Networking, Applications and Worksharing, 2023.
[72]
T.S. Brisimi, A. Olshevsky, and I.C. Paschalidis, "Federated learning of predictive models from healthcare data", IEEE Transactions on Medical Informatics., vol. 30, no. 5, pp. 1265-1280, 2023.
[73]
T. Gafni, N. Shlezinger, K. Cohen, Y.C. Eldar, H.V. Poor, and M. Ben-David, "Federated Learning: A signal processing perspective", IEEE Signal Process. Mag., vol. 39, no. 3, pp. 14-41, 2022.
[http://dx.doi.org/10.1109/MSP.2021.3125282]
[74]
N. Rieke, J. Hancox, W. Li, F. Milletarì, H.R. Roth, S. Albarqouni, S. Bakas, M.N. Galtier, B.A. Landman, K. Maier-Hein, S. Ourselin, M. Sheller, R.M. Summers, A. Trask, D. Xu, M. Baust, and M.J. Cardoso, "The future of digital health with federated learning", NPJ Digit. Med., vol. 3, no. 1, p. 119, 2020.
[http://dx.doi.org/10.1038/s41746-020-00323-1]
[75]
D.C. Nguyen, M. Ding, P.N. Pathirana, A. Seneviratne, J. Li, and H. Vincent Poor, "Federated learning for internet of things: A comprehensive survey", IEEE Commun. Surv. Tutor., vol. 23, no. 3, pp. 1622-1658, 2021.
[http://dx.doi.org/10.1109/COMST.2021.3075439]
[76]
M. Aledhari, R. Razzak, R.M. Parizi, and F. Saeed, "Federated learning: A survey on enabling technologies, protocols, and applications", IEEE Access, vol. 8, pp. 140699-140725, 2020.
[http://dx.doi.org/10.1109/ACCESS.2020.3013541]
[77]
X. Zhang, X. Wu, B. Li, and Y. Liu, "Privacy-preserving federated learning and applications: A comprehensive review", J. Comput. Secur., vol. 48, p. 102232, 2021.
[78]
Q. Zheng, T. Zhang, J. Liu, and W. Xu, "Secure federated learning with adversarial noise: An overview", IEEE Trans. Neural Netw. Learn. Syst., vol. 34, no. 6, pp. 2431-2444, 2023.
[79]
L. Liu, K. Zhang, X. Wang, and H. Wang, "Cross-device federated learning: Challenges, trends, and future research directions", IEEE Access, vol. 12, pp. 12387-12404, 2024.
[80]
W.Y.B. Lim, N.C. Luong, D.T. Hoang, Y. Jiao, Y.C. Liang, Q. Yang, D. Niyato, and C. Miao, "Federated learning in mobile edge networks: A comprehensive survey", IEEE Commun. Surv. Tutor., vol. 22, no. 3, pp. 2031-2063, 2020.
[http://dx.doi.org/10.1109/COMST.2020.2986024]
[81]
B. McMahan, "Communication-Efficient Learning of Deep Networks from Decentralized Data", In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS),, 2017, 1273-1282..
[82]
J. Xu, B.S. Glicksberg, C. Su, P. Walker, J. Bian, and F. Wang, "Federated Learning for Healthcare Informatics", J. Healthc. Inform. Res., vol. 5, no. 1, pp. 1-19, 2021.
[http://dx.doi.org/10.1007/s41666-020-00082-4]
[83]
T. Li, A.K. Sahu, A. Talwalkar, and V. Smith, "Federated learning: Challenges, methods, and future directions", IEEE Signal Process. Mag., vol. 37, no. 3, pp. 50-60, 2020.
[http://dx.doi.org/10.1109/MSP.2020.2975749]
[84]
T.S. Brisimi, R. Chen, T. Mela, A. Olshevsky, I.C. Paschalidis, and W. Shi, "Federated learning of predictive models from federated Electronic Health Records", Int. J. Med. Inform., vol. 112, pp. 59-67, 2018.
[http://dx.doi.org/10.1016/j.ijmedinf.2018.01.007]
[85]
T. Gafni, S. Shalev-Shwartz, and L. Katzir, "Signal processing perspectives in federated learning systems", IEEE Trans. Signal Process., vol. 71, pp. 202-218, 2023.
[http://dx.doi.org/10.1109/TSP.2023.3242074]
[86]
A. Nassif, S. Azam, and A. Abbas, "A review of the future of federated learning in healthcare", IEEE Transactions on Medical Informatics., vol. 42, no. 3, pp. 810-822, 2023.
[87]
S. Fu, H. Jia, M. Vassilaki, V.K. Keloth, Y. Dang, Y. Zhou, M. Garg, R.C. Petersen, J. St Sauver, S. Moon, L. Wang, A. Wen, F. Li, H. Xu, C. Tao, J. Fan, H. Liu, and S. Sohn, "FedFSA: Hybrid and federated framework for functional status ascertainment across institutions", J. Biomed. Inform., vol. 152, p. 104623, 2024.
[http://dx.doi.org/10.1016/j.jbi.2024.104623]
[88]
W. Zhang, T. Wang, and X. Li, "Enabling federated learning in the industrial internet of things: Challenges, solutions, and future directions", J. Ind. Inf. Integr., vol. 30, p. 100380, 2023.
[89]
H.T. Nguyen, V. Sehwag, S. Hosseinalipour, C.G. Brinton, M. Chiang, and H. Vincent Poor, "Fast-convergent federated learning", IEEE J. Sel. Areas Comm., vol. 39, no. 1, pp. 201-218, 2021.
[http://dx.doi.org/10.1109/JSAC.2020.3036952]
[90]
D. Mistry, M.F. Mridha, M. Safran, S. Alfarhood, A.K. Saha, and D. Che, "Privacy-preserving on-screen activity tracking and classification in e-learning using federated learning", IEEE Access, vol. 11, pp. 79315-79329, 2023.
[http://dx.doi.org/10.1109/ACCESS.2023.3299331]
[91]
B. Ghimire, and D.B. Rawat, "Recent advances on federated learning for cybersecurity and cybersecurity for federated learning for Internet of Things", IEEE Internet Things J., vol. 9, no. 11, pp. 8229-8249, 2022.
[http://dx.doi.org/10.1109/JIOT.2022.3150363]
[92]
A.K. Bashir, N. Victor, S. Bhattacharya, T. Huynh-The, R. Chengoden, G. Yenduri, P.K.R. Maddikunta, Q-V. Pham, T.R. Gadekallu, and M. Liyanage, "Federated learning for the healthcare metaverse: Concepts, applications, challenges, and future directions", IEEE Internet Things J., vol. 10, no. 24, pp. 21873-21891, 2023.
[http://dx.doi.org/10.1109/JIOT.2023.3304790]
[93]
G. Long, B. Yang, T. Zhang, Y. Chen, and S. Zhou, "Federated learning for open banking", In: Federated Learning: Privacy and Incentive., Springer International Publishing: Cham, 2020, pp. 240-254.
[http://dx.doi.org/10.1007/978-3-030-63076-8_17]

Rights & Permissions Print Cite
Article Metrics
Pdf icon 17
Html icon 2
Find your institution