Asynchronous federated learning on heterogeneous devices: A survey

Computer Science Review - Tập 50 - Trang 100595 - 2023
Chenhao Xu1, Youyang Qu2,3, Yong Xiang1, Longxiang Gao2,3
1School of Information Technology, Deakin University, Geelong, VIC, Australia
2Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
3Shandong Provincial Key Laboratory of Computer Networks, Shandong Fundamental Research Center for Computer Science, Jinan, China

Tài liệu tham khảo

Ghahramani, 2015, Probabilistic machine learning and artificial intelligence, Nature, 521, 452, 10.1038/nature14541 Xu, 2020, Reinforcement learning-based control and networking co-design for industrial Internet of Things, IEEE J. Sel. Areas Commun., 38, 885, 10.1109/JSAC.2020.2980909 Nie, 2020, Semi-supervised StyleGAN for disentanglement learning, 7360 Voigt, 2017, The eu general data protection regulation (gdpr) Andrew, 2021, The general data protection regulation in the age of surveillance capitalism, J. Bus. Ethics, 168, 565, 10.1007/s10551-019-04239-z Wachter, 2019, A right to reasonable inferences: re-thinking data protection law in the age of big data and AI, Columbia Bus. Law Rev., 494 Qu, 2018, Privacy of things: Emerging challenges and opportunities in wireless internet of things, IEEE Wirel. Commun., 25, 91, 10.1109/MWC.2017.1800112 Xiong, 2018, Attribute-based privacy-preserving data sharing for dynamic groups in cloud computing, IEEE Syst. J., 13, 2739, 10.1109/JSYST.2018.2865221 Xiong, 2020, Edge-assisted privacy-preserving raw data sharing framework for connected autonomous vehicles, IEEE Wirel. Commun., 27, 24, 10.1109/MWC.001.1900463 Xie, 2018, Data collection for security measurement in wireless sensor networks: A survey, IEEE Internet Things J., 6, 2205, 10.1109/JIOT.2018.2883403 Su, 2020, LVBS: Lightweight vehicular blockchain for secure data sharing in disaster rescue, IEEE Trans. Dependable Secure Comput. Konečnỳ, 2016 Konečnỳ, 2016 Lim, 2020, Federated learning in mobile edge networks: A comprehensive survey, IEEE Commun. Surv. Tutor., 22, 2031, 10.1109/COMST.2020.2986024 Yin, 2021, A comprehensive survey of privacy-preserving federated learning: A taxonomy, review, and future directions, ACM Comput. Surv., 54, 1, 10.1145/3460427 Wahab, 2021, Federated machine learning: Survey, multi-level classification, desirable criteria and future directions in communication and networking systems, IEEE Commun. Surv. Tutor., 23, 1342, 10.1109/COMST.2021.3058573 Imteaj, 2021, A survey on federated learning for resource-constrained IoT devices, IEEE Internet Things J. Khan, 2021, Federated learning for internet of things: Recent advances, taxonomy, and open challenges, IEEE Commun. Surv. Tutor., 10.1109/COMST.2021.3090430 Lyu, 2020 Abdel-Basset, 2022, Federated learning for privacy-preserving internet of things, 215 Wu, 2020, SAFA: A semi-asynchronous protocol for fast federated learning with low overhead, IEEE Trans. Comput., 70, 655, 10.1109/TC.2020.2994391 Verbraeken, 2020, A survey on distributed machine learning, ACM Comput. Surv., 53, 1, 10.1145/3377454 McMahan, 2017, Communication-efficient learning of deep networks from decentralized data, 1273 Liu, 2022, From distributed machine learning to federated learning: A survey, Knowl. Inf. Syst., 64, 885, 10.1007/s10115-022-01664-x Xu, 2023, Scei: A smart-contract driven edge intelligence framework for iot systems, IEEE Trans. Mob. Comput., 10.1109/TMC.2023.3290925 Gu, 2021, Privacy-preserving asynchronous vertical federated learning algorithms for multiparty collaborative learning, IEEE Trans. Neural Netw. Learn. Syst. Trindade, 2021 Nguyen, 2022, Federated learning with buffered asynchronous aggregation, 3581 Liu, 2021, Blockchain-enabled asynchronous federated learning in edge computing, Sensors, 21, 3335, 10.3390/s21103335 Nakamoto, 2008, Bitcoin: A peer-to-peer electronic cash system, Decent. Bus. Rev., 21260 Xu, 2021, A lightweight and attack-proof bidirectional blockchain paradigm for Internet of Things, IEEE Internet Things J., 9, 4371, 10.1109/JIOT.2021.3103275 Kiayias, 2017, Ouroboros: A provably secure proof-of-stake blockchain protocol, 357 Sukhwani, 2017, Performance modeling of PBFT consensus process for permissioned blockchain network (hyperledger fabric), 253 Qu, 2020, A blockchained federated learning framework for cognitive computing in industry 4.0 networks, IEEE Trans. Ind. Inform., 17, 2964, 10.1109/TII.2020.3007817 Qu, 2020, Decentralized privacy using blockchain-enabled federated learning in fog computing, IEEE Internet Things J., 7, 5171, 10.1109/JIOT.2020.2977383 Dwork, 2008, Differential privacy: A survey of results, 1 Dwork, 2014, The algorithmic foundations of differential privacy, Found. Trends Theor. Comput. Sci., 9, 211 Cao, 2021, Data poisoning attacks to local differential privacy protocols, 947 Dong, 2020, Optimal differential privacy composition for exponential mechanisms, 2597 Zhu, 2020, Private-knn: Practical differential privacy for computer vision, 11854 Qu, 2021 Qu, 2020, Customizable reliable privacy-preserving data sharing in cyber-physical social networks, IEEE Trans. Netw. Sci. Eng., 8, 269, 10.1109/TNSE.2020.3036855 Soria-Comas, 2017, Individual differential privacy: A utility-preserving formulation of differential privacy guarantees, IEEE Trans. Inf. Forensics Secur., 12, 1418, 10.1109/TIFS.2017.2663337 Wei, 2020, Federated learning with differential privacy: Algorithms and performance analysis, IEEE Trans. Inf. Forensics Secur., 15, 3454, 10.1109/TIFS.2020.2988575 Liu, 2020, A secure federated learning framework for 5G networks, IEEE Wirel. Commun., 27, 24, 10.1109/MWC.01.1900525 Chen, 2021, Towards asynchronous federated learning for heterogeneous edge-powered internet of things, Digit. Commun. Netw., 10.1016/j.dcan.2021.04.001 Xiao, 2017 Cohen, 2017, EMNIST: Extending MNIST to handwritten letters, 2921 Zhou, 2021, TEA-fed: time-efficient asynchronous federated learning for edge computing, 30 Hao, 2020, Time efficient federated learning with semi-asynchronous communication, 156 Imteaj, 2020, Fedar: Activity and resource-aware federated learning model for distributed mobile robots, 1153 Harrison, 1978, Hedonic housing prices and the demand for clean air, J. Environ. Econ. Manag., 5, 81, 10.1016/0095-0696(78)90006-2 Stolfo, 2000, 1 Hu, 2023, Scheduling and aggregation design for asynchronous federated learning over wireless networks, IEEE J. Sel. Areas Commun., 41, 874, 10.1109/JSAC.2023.3242719 Xie, 2019 Merity, 2016 Chen, 2019, Communication-efficient federated deep learning with layerwise asynchronous model update and temporally weighted aggregation, IEEE Trans. Neural Netw. Learn. Syst., 31, 4229, 10.1109/TNNLS.2019.2953131 Anguita, 2013, A public domain dataset for human activity recognition using smartphones, 3 Shi, 2020, HySync: Hybrid federated learning with effective synchronization, 628 Chen, 2020, Asynchronous online federated learning for edge devices with non-iid data, 15 Ni, 2019, Modeling heart rate and activity data for personalized fitness recommendation, 1343 Luo, 2019, AccuAir: Winning solution to air quality prediction for KDD Cup 2018, 1842 Vaizman, 2017, Recognizing detailed human context in the wild from smartphones and smartwatches, IEEE Pervasive Comput., 16, 62, 10.1109/MPRV.2017.3971131 Xiaofeng, 2020, An asynchronous federated learning mechanism for edge network computing, J. Comput. Res. Dev., 57, 2571 Wang, 2021, Efficient federated learning for fault diagnosis in industrial cloud-edge computing, Computing, 1 Loparo, 2013 Cao, 2018, Gear fault data, Figshare Lu, 2020, Privacy-preserving asynchronous federated learning mechanism for edge network computing, IEEE Access, 8, 48970, 10.1109/ACCESS.2020.2978082 Li, 2020, Efficient asynchronous vertical federated learning via gradient prediction and double-end sparse compression, 291 Dewi, 2019, Analysis accuracy of random forest model for big data–A case study of claim severity prediction in car insurance, 60 Yeh, 2009, The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients, Expert Syst. Appl., 36, 2473, 10.1016/j.eswa.2007.12.020 Lee, 2021 A. Go, R. Bhayani, L. Huang, Twitter Sentiment Classification using Distant Supervision, Vol. 1, CS224N project report, Stanford, 2009, p. 2009, (12). Liu, 2015, Deep learning face attributes in the wild, 3730 Stripelis, 2021 So, 2021 Chai, 2020 Zhang, 2021 Shamir, 2014, Communication-efficient distributed optimization using an approximate newton-type method, 1000 Xia, 2021 Lee, 2020 Sun, 2020, Adaptive federated learning and digital twin for industrial internet of things, IEEE Trans. Ind. Inform., 17, 5605, 10.1109/TII.2020.3034674 Fadlullah, 2020, HCP: Heterogeneous computing platform for federated learning based collaborative content caching towards 6G networks, IEEE Trans. Emerg. Top. Comput. Aji, 2017 Zhu, 2020, Deep leakage from gradients, 17 Sattler, 2020, Clustered federated learning: Model-agnostic distributed multitask optimization under privacy constraints, IEEE Trans. Neural Netw. Learn. Syst. Stripelis, 2020 Tian, 2021, Towards asynchronous federated learning based threat detection: a DC-adam approach, Comput. Secur., 10.1016/j.cose.2021.102344 Chen, 2021, FedSA: A staleness-aware asynchronous Federated Learning algorithm with non-IID data, Future Gener. Comput. Syst., 120, 1, 10.1016/j.future.2021.02.012 Diwangkara, 2020, Study of data imbalance and asynchronous aggregation algorithm on federated learning system, 276 Avdiukhin, 2021, Federated learning under arbitrary communication patterns, 425 Yang, 2020, Prototyping federated learning on edge computing systems, Front. Comput. Sci., 14, 10.1007/s11704-019-9237-3 Chen, 2020 Defazio, 2014, SAGA: A fast incremental gradient method with support for non-strongly convex composite objectives, 1646 Zhang, 2021, Secure bilevel asynchronous vertical federated learning with backward updating, 10896 Shokri, 2017, Membership inference attacks against machine learning models, 3 Melis, 2019, Exploiting unintended feature leakage in collaborative learning, 691 Fredrikson, 2015, Model inversion attacks that exploit confidence information and basic countermeasures, 1322 Lu, 2019, Differentially private asynchronous federated learning for mobile edge computing in urban informatics, IEEE Trans. Ind. Inform., 16, 2134, 10.1109/TII.2019.2942179 Li, 2019 van Dijk, 2020 Lu, 2020, Communication-efficient federated learning and permissioned blockchain for digital twin edge networks, IEEE Internet Things J., 8, 2276, 10.1109/JIOT.2020.3015772 Lu, 2020, Communication-efficient federated learning for digital twin edge networks in industrial iot, IEEE Trans. Ind. Inform., 17, 5709, 10.1109/TII.2020.3010798 Lu, 2020, Blockchain empowered asynchronous federated learning for secure data sharing in internet of vehicles, IEEE Trans. Veh. Technol., 69, 4298, 10.1109/TVT.2020.2973651 Feng, 2021, Blockchain-based asynchronous federated learning for internet of things, IEEE Trans. Comput. Yuan, 2021, ChainsFL: Blockchain-driven federated learning from design to realization, 1 Xu, 2021, Bafl: An efficient blockchain-based asynchronous federated learning framework, 1 Xu, 2022, An efficient and reliable asynchronous federated learning scheme for smart public transportation, IEEE Trans. Veh. Technol. Xu, 2022, BASS: Blockchain-based asynchronous SignSGD for robust collaborative data mining, 1 Zhang, 2021 Yang, 2021, Privacy-preserving federated learning for UAV-enabled networks: Learning-based joint scheduling and resource management, IEEE J. Sel. Areas Commun., 10.1109/JSAC.2021.3088655 Ma, 2021, An asynchronous and real-time update paradigm of federated learning diagnosisfor fault, IEEE Trans. Ind. Inform. Kall, 2021, An asynchronous federated learning approach for a security source code scanner, 572 Chen, 2021 Sprague, 2018, Asynchronous federated learning for geospatial applications, 21 Khodak, 2019, Adaptive gradient-based meta-learning methods, Adv. Neural Inf. Process. Syst., 32 Smith, 2017, Federated multi-task learning, Adv. Neural Inf. Process. Syst., 30 Qu, 2018, Improving data utility through game theory in personalized differential privacy, 1 Qu, 2020, Gan-driven personalized spatial-temporal private data sharing in cyber-physical social systems, IEEE Trans. Netw. Sci. Eng., 7, 2576, 10.1109/TNSE.2020.3001061 Gai, 2018, Proof of reputation: A reputation-based consensus protocol for peer-to-peer network, 666 Huang, 2019, Performance analysis of the raft consensus algorithm for private blockchains, IEEE Trans. Syst. Man Cybern.: Syst., 50, 172, 10.1109/TSMC.2019.2895471 Gilad, 2017, Algorand: Scaling byzantine agreements for cryptocurrencies, 51 Hohenberger, 2005, How to securely outsource cryptographic computations, 264 Armknecht, 2015, A guide to fully homomorphic encryption, IACR Cryptol. ePrint Arch., 2015, 1192 Li, 2020, Federated Learning-Based Ultra-Short term load forecasting in power Internet of things, 63