References
[1]. Konečný, Jakub, et al. "Federated learning: Strategies for improving communication efficiency." arXiv preprint arXiv:1610.05492 (2016).
[2]. McMahan, Brendan, et al. "Communication-efficient learning of deep networks from decentralized data." Artificial intelligence and statistics. PMLR, 2017.
[3]. Ho, Qirong, et al. "More effective distributed ml via a stale synchronous parallel parameter server." Advances in neural information processing systems (2013).
[4]. Li, Mu, et al. "Scaling distributed machine learning with the parameter server." 11th USENIX Symposium on operating systems design and implementation (OSDI 14). 2014.
[5]. Li, Mu, et al. "Communication efficient distributed machine learning with the parameter server." Advances in Neural Information Processing Systems27 (2014).
[6]. McMahan, H. Brendan, et al. "Learning differentially private recurrent language models." arXiv preprint arXiv:1710.06963 (2017).
[7]. Bonawitz, Keith, et al. "Practical secure aggregation for privacy-preserving machine learning." Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security. 2017.
[8]. Wu, Wentai, et al. "SAFA: A semi-asynchronous protocol for fast federated learning with low overhead." IEEE Transactions on Computers 70.5 (2020): 655-668.
[9]. Bonawitz, Keith, et al. "Towards federated learning at scale: System design." Proceedings of machine learning and systems 1 (2019): 374-388.
[10]. Xie, Cong, Sanmi Koyejo, and Indranil Gupta. "Asynchronous federated optimization." arXiv preprint arXiv:1903.03934 (2019).
[11]. Lian, Xiangru, et al. "Asynchronous decentralized parallel stochastic gradient descent." International Conference on Machine Learning. PMLR, 2018.
[12]. Zheng, Shuxin, et al. "Asynchronous stochastic gradient descent with delay compensation." International Conference on Machine Learning. PMLR, 2017.
[13]. Zinkevich, Martin, et al. "Parallelized stochastic gradient descent." Advances in neural information processing systems 23 (2010).
[14]. Dean, Jeffrey, et al. "Large scale distributed deep networks." Advances in neural information processing systems 25 (2012).
[15]. Kairouz, Peter, et al. "Advances and open problems in federated learning." Foundations and Trends® in Machine Learning 14.1–2 (2021): 1-210.
[16]. Sattler, Felix, et al. "Robust and communication-efficient federated learning from non-iid data." IEEE transactions on neural networks and learning systems 31.9 (2019): 3400-3413.
[17]. Zhao, Yue, et al. "Federated learning with non-iid data." arXiv preprint arXiv:1806.00582 (2018).https://www.tensorflow.org/federated
[18]. https://www.tensorflow.org/federated
[19]. Recht, Benjamin, et al. "Hogwild!: A lock-free approach to parallelizing stochastic gradient descent." Advances in neural information processing systems 24 (2011).
[20]. Sprague, Michael R., et al. "Asynchronous federated learning for geospatial applications." Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Cham: Springer International Publishing, 2018.
[21]. Sprague, Michael R., et al. "Asynchronous federated learning for geospatial applications." Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Cham: Springer International Publishing, 2018.
[22]. Zhou, Chendi, et al. "TEA-fed: time-efficient asynchronous federated learning for edge computing." Proceedings of the 18th ACM International Conference on Computing Frontiers. 2021.
[23]. Shi, Guomei, et al. "HySync: Hybrid federated learning with effective synchronization." 2020 IEEE 22nd International Conference on High Performance Computing and Communications; IEEE 18th International Conference on Smart City; IEEE 6th International Conference on Data Science and Systems (HPCC/SmartCity/DSS). IEEE, 2020.
[24]. Chen, Yujing, et al. "Asynchronous online federated learning for edge devices with non-iid data." 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020.
[25]. Chen, Jianmin, et al. "Revisiting distributed synchronous SGD." arXiv preprint arXiv:1604.00981(2016).
[26]. Nishio, Takayuki, and Ryo Yonetani. "Client selection for federated learning with heterogeneous resources in mobile edge." ICC 2019-2019 IEEE international conference on communications (ICC). IEEE, 2019.
[27]. Chai, Zheng, et al. "Fedat: A communication-efficient federated learning method with asynchronous tiers under non-iid data." ArXivorg (2020).
[28]. Nguyen, John, et al. "Federated learning with buffered asynchronous aggregation." International Conference on Artificial Intelligence and Statistics. PMLR, 2022.
[29]. So, Jinhyun, et al. "Secure aggregation for buffered asynchronous federated learning." arXiv preprint arXiv:2110.02177 (2021).
[30]. Zhang, Tuo, et al. "Federated learning for the internet of things: Applications, challenges, and opportunities." IEEE Internet of Things Magazine 5.1 (2022): 24-29.
[31]. Sakib, Sadman, et al. "On COVID-19 prediction using asynchronous federated learning-based agile radiograph screening booths." ICC 2021-IEEE International Conference on Communications. IEEE, 2021.
[32]. Yaqoob, Muhammad Mateen, et al. "Modified Artificial Bee Colony Based Feature Optimized Federated Learning for Heart Disease Diagnosis in Healthcare." Applied Sciences 12.23 (2022): 12080.
[33]. Sakib, Sadman, et al. "Asynchronous federated learning-based ECG analysis for arrhythmia detection." 2021 IEEE International Mediterranean Conference on Communications and Networking (MeditCom). IEEE, 2021.
[34]. Khan, Muhammad Amir, et al. "Asynchronous Federated Learning for Improved Cardiovascular Disease Prediction Using Artificial Intelligence." Diagnostics 13.14 (2023): 2340.
Cite this article
Liu,Y. (2024). Asynchronous Federated Learning: Methods and applications. Applied and Computational Engineering,39,122-129.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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References
[1]. Konečný, Jakub, et al. "Federated learning: Strategies for improving communication efficiency." arXiv preprint arXiv:1610.05492 (2016).
[2]. McMahan, Brendan, et al. "Communication-efficient learning of deep networks from decentralized data." Artificial intelligence and statistics. PMLR, 2017.
[3]. Ho, Qirong, et al. "More effective distributed ml via a stale synchronous parallel parameter server." Advances in neural information processing systems (2013).
[4]. Li, Mu, et al. "Scaling distributed machine learning with the parameter server." 11th USENIX Symposium on operating systems design and implementation (OSDI 14). 2014.
[5]. Li, Mu, et al. "Communication efficient distributed machine learning with the parameter server." Advances in Neural Information Processing Systems27 (2014).
[6]. McMahan, H. Brendan, et al. "Learning differentially private recurrent language models." arXiv preprint arXiv:1710.06963 (2017).
[7]. Bonawitz, Keith, et al. "Practical secure aggregation for privacy-preserving machine learning." Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security. 2017.
[8]. Wu, Wentai, et al. "SAFA: A semi-asynchronous protocol for fast federated learning with low overhead." IEEE Transactions on Computers 70.5 (2020): 655-668.
[9]. Bonawitz, Keith, et al. "Towards federated learning at scale: System design." Proceedings of machine learning and systems 1 (2019): 374-388.
[10]. Xie, Cong, Sanmi Koyejo, and Indranil Gupta. "Asynchronous federated optimization." arXiv preprint arXiv:1903.03934 (2019).
[11]. Lian, Xiangru, et al. "Asynchronous decentralized parallel stochastic gradient descent." International Conference on Machine Learning. PMLR, 2018.
[12]. Zheng, Shuxin, et al. "Asynchronous stochastic gradient descent with delay compensation." International Conference on Machine Learning. PMLR, 2017.
[13]. Zinkevich, Martin, et al. "Parallelized stochastic gradient descent." Advances in neural information processing systems 23 (2010).
[14]. Dean, Jeffrey, et al. "Large scale distributed deep networks." Advances in neural information processing systems 25 (2012).
[15]. Kairouz, Peter, et al. "Advances and open problems in federated learning." Foundations and Trends® in Machine Learning 14.1–2 (2021): 1-210.
[16]. Sattler, Felix, et al. "Robust and communication-efficient federated learning from non-iid data." IEEE transactions on neural networks and learning systems 31.9 (2019): 3400-3413.
[17]. Zhao, Yue, et al. "Federated learning with non-iid data." arXiv preprint arXiv:1806.00582 (2018).https://www.tensorflow.org/federated
[18]. https://www.tensorflow.org/federated
[19]. Recht, Benjamin, et al. "Hogwild!: A lock-free approach to parallelizing stochastic gradient descent." Advances in neural information processing systems 24 (2011).
[20]. Sprague, Michael R., et al. "Asynchronous federated learning for geospatial applications." Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Cham: Springer International Publishing, 2018.
[21]. Sprague, Michael R., et al. "Asynchronous federated learning for geospatial applications." Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Cham: Springer International Publishing, 2018.
[22]. Zhou, Chendi, et al. "TEA-fed: time-efficient asynchronous federated learning for edge computing." Proceedings of the 18th ACM International Conference on Computing Frontiers. 2021.
[23]. Shi, Guomei, et al. "HySync: Hybrid federated learning with effective synchronization." 2020 IEEE 22nd International Conference on High Performance Computing and Communications; IEEE 18th International Conference on Smart City; IEEE 6th International Conference on Data Science and Systems (HPCC/SmartCity/DSS). IEEE, 2020.
[24]. Chen, Yujing, et al. "Asynchronous online federated learning for edge devices with non-iid data." 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020.
[25]. Chen, Jianmin, et al. "Revisiting distributed synchronous SGD." arXiv preprint arXiv:1604.00981(2016).
[26]. Nishio, Takayuki, and Ryo Yonetani. "Client selection for federated learning with heterogeneous resources in mobile edge." ICC 2019-2019 IEEE international conference on communications (ICC). IEEE, 2019.
[27]. Chai, Zheng, et al. "Fedat: A communication-efficient federated learning method with asynchronous tiers under non-iid data." ArXivorg (2020).
[28]. Nguyen, John, et al. "Federated learning with buffered asynchronous aggregation." International Conference on Artificial Intelligence and Statistics. PMLR, 2022.
[29]. So, Jinhyun, et al. "Secure aggregation for buffered asynchronous federated learning." arXiv preprint arXiv:2110.02177 (2021).
[30]. Zhang, Tuo, et al. "Federated learning for the internet of things: Applications, challenges, and opportunities." IEEE Internet of Things Magazine 5.1 (2022): 24-29.
[31]. Sakib, Sadman, et al. "On COVID-19 prediction using asynchronous federated learning-based agile radiograph screening booths." ICC 2021-IEEE International Conference on Communications. IEEE, 2021.
[32]. Yaqoob, Muhammad Mateen, et al. "Modified Artificial Bee Colony Based Feature Optimized Federated Learning for Heart Disease Diagnosis in Healthcare." Applied Sciences 12.23 (2022): 12080.
[33]. Sakib, Sadman, et al. "Asynchronous federated learning-based ECG analysis for arrhythmia detection." 2021 IEEE International Mediterranean Conference on Communications and Networking (MeditCom). IEEE, 2021.
[34]. Khan, Muhammad Amir, et al. "Asynchronous Federated Learning for Improved Cardiovascular Disease Prediction Using Artificial Intelligence." Diagnostics 13.14 (2023): 2340.