Federated Learning with Swift: An Extension of Flower and Performance Evaluation

Parikh, 2019, Security and privacy issues in cloud, fog and edge computing, Procedia Comput Sci, 160, 734, 10.1016/j.procs.2019.11.018 Xiao, 2012, Security and privacy in cloud computing, IEEE Commun Surv Tutor, 15, 843, 10.1109/SURV.2012.060912.00182 Yang, 2020, Data security and privacy protection for cloud storage: A survey, IEEE Access, 8, 131723, 10.1109/ACCESS.2020.3009876 Russo, 2018, Cloud computing and the new EU general data protection regulation, IEEE Cloud Comput, 5, 58, 10.1109/MCC.2018.064181121 Lim, 2020, Federated learning in mobile edge networks: A comprehensive survey, IEEE Commun Surv Tutor, 22, 2031, 10.1109/COMST.2020.2986024 Cao, 2020, An overview on edge computing research, IEEE Access, 8, 85714, 10.1109/ACCESS.2020.2991734 Ignatov, 2019, Ai benchmark: All about deep learning on smartphones in 2019, 3617 Wang, 2020, Neural network inference on mobile socs, IEEE Des Test, 37, 50, 10.1109/MDAT.2020.2968258 Xu Z, Li L, Zou W. Exploring federated learning on battery-powered devices. In: Proceedings of the ACM turing celebration conference. 2019, p. 1–6. Kulkarni, 2020, Survey of personalization techniques for federated learning, 794 Yu, 2020 McMahan, 2017, Communication-efficient learning of deep networks from decentralized data, 1273 Hard, 2018 Kairouz, 2021, Advances and open problems in federated learning, Found Trends Mach Learn, 14, 1, 10.1561/2200000083 Huhn, 2022, The impact of wearable technologies in health research: scoping review, JMIR mHealth and uHealth, 10, 10.2196/34384 Iqbal, 2021, Advances in healthcare wearable devices, NPJ Flex Electron, 5, 9, 10.1038/s41528-021-00107-x Ziller, 2021, Pysyft: A library for easy federated learning, 111 Bonawitz, 2019, Towards federated learning at scale: System design, Proc Mach Learn Syst, 1, 374 2019 Lai, 2022, Fedscale: Benchmarking model and system performance of federated learning at scale, 11814 Beutel, 2020 Nguyen, 2019, Machine learning and deep learning frameworks and libraries for large-scale data mining: a survey, Artif Intell Rev, 52, 77, 10.1007/s10462-018-09679-z Liu, 2011, Status and trends of mobile-health applications for iOS devices: A developer’s perspective, J Syst Softw, 84, 2022, 10.1016/j.jss.2011.06.049 LeCun, 1998 Krizhevsky, 2010 Ma, 2021, Federated learning with unreliable clients: Performance analysis and mechanism design, IEEE Internet Things J, 8, 17308, 10.1109/JIOT.2021.3079472 Bonawitz K, Ivanov V, Kreuter B, Marcedone A, McMahan HB, Patel S, et al. Practical secure aggregation for privacy-preserving machine learning. In: Proceedings of the 2017 ACM SIGSAC conference on computer and communications security. 2017, p. 1175–91.