Li, Tian, et al.”Federated learning: Challenges, methods, and future directions.” IEEE signal processing magazine 37.3 (2020): 50-60.
Zhang, Jiale, et al.”Poisoning attack in federated learning using generative adversarial nets.” 2019 18th IEEE international conference on trust, security and privacy in computing and communications/13th IEEE international conference on big data science and engineering (TrustCom/BigDataSE). IEEE, 2019.
Kairouz, Peter, et al.”Advances and open problems in federated learning.” Foundations and trends® in machine learning 14.1–2 (2021): 1-210.
Lalitha, Anusha, et al.”Fully decentralized federated learning.” Third workshop on bayesian deep learning (NeurIPS). Vol. 2. 2018.
Beltrán, Enrique Tomás Martínez, et al.”Decentralized federated learning: Fundamentals, state of the art, frameworks, trends, and challenges.” IEEE Communications Surveys & Tutorials (2023).
Yuan, Liangqi, et al.”Decentralized federated learning: A survey and perspective.” arXiv preprint arXiv:2306.01603 (2023).
Elmrabit, Nebrase, et al.”Evaluation of machine learning algorithms for anomaly detection.” 2020 international conference on cyber security and protection of digital services (cyber security). IEEE, 2020.
Ait Mahammed, Fatima.”Approches d’apprentissage automatique pour la détection du Spam Web: exploration de diverses caractéristiques.” (2018).
Rainio, O., Teuho, J. & Klén, R. Evaluation metrics and statistical tests for machine learning. Sci Rep 14, 6086 (2024)
Martinez, M., & Stiefelhagen, R. Taming the cross entropy loss. In Pattern Recognition: 40th German Conference, GCPR 2018, Stuttgart, Germany, October 9-12, 2018, Proceedings 628–637, Vol. 40. Springer (2019).
Beltrán, Enrique Tomás Martínez, et al.”Fedstellar: A platform for decentralized federated learning.” Expert Systems with Applications 242 (2024): 122861.
Popescu, Marius-Constantin, et al.”Multilayer perceptron and neural networks.” WSEAS Transactions on Circuits and Systems 8.7 (2009): 579-588.
Konecný, Jakub, et al.”Federated learning: Strategies for improving communication efficiency.” arXiv preprint arXiv:1610.05492 8 (2016).
Bonawitz, K., Eichner, H., Grieskamp, W., et al.: Towards federated learning at scale: System design. arXiv preprint arXiv:1902.01046 (2019)
Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H.B., Patel, S., Ramage, D., Segal, A., Seth, K.: Practical secure aggregation for privacy-preserving machine learning. In: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security. pp. 1175–1191. ACM (2017)
T. Li, A. K. Sahu, A. Talwalkar, and V. Smith, “Federated learning: Challenges, methods, and future directions,” IEEE Signal Processing Magazine, vol. 37, no. 3, pp. 50–60, 2020.
W. Y. B. Lim et al., “Federated learning in mobile edge networks: A comprehensive survey,” IEEE Communications Surveys & Tutorials, vol. 22, no. 3, pp. 2031–2063, 2020.
D. C. Nguyen, M. Ding, P. N. Pathirana, A. Seneviratne, J. Li, and H. V. Poor, “Federated learning for Internet of Things: A comprehensive survey,” IEEE Communications Surveys & Tutorials, vol. 23, no. 3, pp. 1622–1658, 2021.
L. U. Khan, W. Saad, Z. Han, E. Hossain, and C. S. Hong, “Federated learning for Internet of Things: Recent advances, taxonomy, and open challenges,” IEEE Communications Surveys & Tutorials, vol. 23, no. 3, pp. 1759–1799, 2021.
V. Mothukuri, R. M. Parizi, S. Pouriyeh, Y. Huang, A. Dehghantanha, and G. Srivastava, “A survey on security and privacy of federated learning,” Future Generation Computer Systems, vol. 115, pp. 619– 640, 2021.
P. Boobalan et al., “Fusion of federated learning and industrial Internet of Things: A survey,” Computer Networks, vol. 212, p. 109048, 2022.
Joshi, Madhura, Ankit Pal, and Malaikannan Sankarasubbu.”Federated learning for healthcare domain-pipeline, applications and challenges.” ACM Transactions on Computing for Healthcare 3.4 (2022): 1-36.
L. Witt, M. Heyer, K. Toyoda, W. Samek, and D. Li, “Decentral and incentivized federated learning frameworks: A systematic literature review,” arXiv preprint arXiv:2205.07855, 2022.
Qu, Youyang, et al.”Decentralized privacy using blockchain-enabled federated learning in fog computing.” IEEE Internet of Things Journal 7.6 (2020): 5171-5183.
M. Billah, S. T. Mehedi, A. Anwar, Z. Rahman, and R. Islam, “A systematic literature review on blockchain enabled federated learning framework for Internet of Vehicles,” arXiv preprint arXiv:2203.05192, 2022.
R. Gupta and T. Alam, “Survey on federated-learning approaches in distributed environment,” Wireless Personal Communications, Mar 2022.
D. Saraswat et al., “Blockchain-based federated learning in UAVs beyond 5G networks: A solution taxonomy and future directions,” IEEE Access, vol. 10, pp. 33 154–33 182, 2022.
Qu, Youyang, et al.”Blockchain-enabled federated learning: A survey.” ACM Computing Surveys 55.4 (2022): 1-35.
Yemini, Michal, et al.”Semi-decentralized federated learning with collaborative relaying.” 2022 IEEE International Symposium on Information Theory (ISIT). IEEE, 2022.
Roy, Abhijit Guha, et al.”Braintorrent: A peer-to-peer environment for decentralized federated learning.” arXiv preprint arXiv:1905.06731 (2019).
Kaur, Davinder, et al.”Trustworthy artificial intelligence: a review.” ACM computing surveys (CSUR) 55.2 (2022): 1-38.