A Systematic Literature Review on Federated Machine Learning: From a Software Engineering Perspective
Open accessAbstract
Federated learning is an emerging machine learning paradigm where clients train models locally and formulate a global model based on the local model updates. To identify the state-of-the-art in federated learning and explore how to develop federated learning systems, we perform a systematic literature review from a software engineering perspective, based on 231 primary studies. Our data synthesis covers the lifecycle of federated learning system development that includes background understanding, requirement analysis, architecture design, implementation, and evaluation. We highlight and summarise the findings from the results and identify future trends to encourage researchers to advance their current work.
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The protocol in [194] achieves these properties by using a combination of sparsification, ternarization, error accumulation, and optimal Golomb encoding techniques for uplink compression and speeding up parallel training in the global server without compromising learning convergence.
“These properties can be achieved by using a combination of sparsification, ternarization, error accumulation, and optimal Golomb encoding techniques for uplink compression and speeding up parallel training in the global server without compromising learning convergence.”
From Federated Learning for Internet of Things: A Comprehensive Survey (2021) · cites this paper as [194]
The research in [194] proposes a new efficient communication protocol that is able to compress uplink and downlink communications while remaining highly robust to the increased number of clients and data distribution.
“The research in [194] proposes a new efficient communication protocol that is able to compress uplink and downlink communications while remaining high robustness to the increased number of clients, and data distribution.”
From Federated Learning for Internet of Things: A Comprehensive Survey (2021) · cites this paper as [194]
Some literature reviews in related work focus on a specific application context such as edge/cloud/hybrid architectures, ML-based software analytics and business intelligence applications, or federated learning.
“Secondly, some literature reviews in related work focus on a specific application context such as edge/cloud/hybrid architectures [15], ML-based software analytics and business intelligence applications [6], or federated learning [25].”
From The pipeline for the continuous development of artificial intelligence models—Current state of research and practice (2023) · cites this paper as [25]
Cites
Federated Machine Learning: Concept and Applications
Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3298981 · Cited by 6,322 · see citing works
Federated learning: Challenges, methods, and future directions
Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020) · IEEE Signal Processing Magazine · doi:10.1109/msp.2020.2975749 · Cited by 6,210 · see citing works
Practical Secure Aggregation for Privacy-Preserving Machine Learning
Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H. B., Patel, S., Ramage, D., Segal, A., & Seth, K. (2017) · Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3133956.3133982 · Cited by 3,956 · see citing works
Adaptive Federated Learning in Resource Constrained Edge Computing Systems
Wang, S., Tuor, T., Salonidis, T., Leung, K. K., Makaya, C., He, T., & Chan, K. (2019) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2019.2904348 · Cited by 2,232 · see citing works
Robust and Communication-Efficient Federated Learning From Non-i.i.d. Data
Sattler, F., Wiedemann, S., Muller, K.-R., & Samek, W. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2944481 · Cited by 1,725 · see citing works
Sattler, F., Muller, K.-R., & Samek, W. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3015958 · Cited by 1,355 · see citing works
Blockchain and Federated Learning for Privacy-Preserved Data Sharing in Industrial IoT
Lu, Y., Huang, X., Dai, Y., Maharjan, S., & Zhang, Y. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2942190 · Cited by 1,245 · see citing works
Federated Learning via Over-the-Air Computation
Yang, K., Jiang, T., Shi, Y., & Ding, Z. (2020) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2019.2961673 · Cited by 1,084 · cited 5× · see citing works
“ral server: Physically, the central servers are usually hosted on a local machine [89, 194], cloud server [57, 155, 178], mobile edge computing platform [122], edge gateway [147, 207] or base station [9, 66, 107, 184]. Most studies propose the central server as a hardware component that creates the first global model…”
BackgroundMethodInfluentialA Hybrid Approach to Privacy-Preserving Federated Learning
Truex, S., Baracaldo, N., Anwar, A., Steinke, T., Ludwig, H., Zhang, R., & Zhou, Y. (2019) · Proceedings of the 12th ACM Workshop on Artificial Intelligence and Security · doi:10.1145/3338501.3357370 · Cited by 1,029 · see citing works
The General Data Protection Regulation
Thelisson, E., Sharma, K., Salam, H., & Dignum, V. (2018) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3170427.3170632 · Cited by 991 · see citing works
Kang, J., Xiong, Z., Niyato, D. T., Xie, S., & Zhang, J. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2940820 · Cited by 961 · see citing works
Requirements engineering paper classification and evaluation criteria: a proposal and a discussion
Wieringa, R., Maiden, N., Mead, N., & Rolland, C. (2006) · Requirements Engineering · doi:10.1007/s00766-005-0021-6 · Cited by 940 · see citing works
Blockchained On-Device Federated Learning
Kim, H., Park, J., Bennis, M., & Kim, S.-L. (2020) · IEEE Communications Letters · doi:10.1109/lcomm.2019.2921755 · Cited by 887 · see citing works
Verifynet: Secure and verifiable federated learning
Xu, G., Li, H., Liu, S., Yang, K., & Lin, X. (2020) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2019.2929409 · Cited by 839 · see citing works
Broadband Analog Aggregation for Low-Latency Federated Edge Learning
Zhu, G., Wang, Y., & Huang, K. (2019) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2019.2946245 · Cited by 786 · see citing works
Reliable Federated Learning for Mobile Networks
Kang, J., Xiong, Z., Niyato, D., Zou, Y., Zhang, Y., & Guizani, M. (2020) · IEEE Wireless Communications · doi:10.1109/mwc.001.1900119 · Cited by 617 · see citing works
DeepChain: Auditable and Privacy-Preserving Deep Learning with Blockchain-based Incentive
Weng, J., Weng, J., Zhang, J., Li, M., Zhang, Y., & Luo, W. (2019) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2019.2952332 · Cited by 610 · cited 5× · see citing works
“ture Review on Federated Machine Learning: From A Software Engineering Perspective 111:13 Fig. 6. Research Area Trend The interests in data security (e.g., [19, 49]) and client device security (e.g., [35, 48, 171]) are also significantly high. The data security in federated learning can be understood as the degree to…”
BackgroundMethodInfluentialA Learning-Based Incentive Mechanism for Federated Learning
Zhan, Y., Li, P., Qu, Z., Zeng, D., & Guo, S. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2967772 · Cited by 602 · see citing works
Chen, Y., Sun, X., & Jin, Y. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2953131 · Cited by 575 · see citing works
Efficient and Privacy-Enhanced Federated Learning for Industrial Artificial Intelligence
Hao, M., Li, H., Luo, X., Xu, G., Yang, H., & Liu, S. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2945367 · Cited by 569 · see citing works
Asynchronous Online Federated Learning for Edge Devices with Non-IID Data
Chen, Y., Ning, Y., Slawski, M., & Rangwala, H. (2020) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata50022.2020.9378161 · Cited by 475 · see citing works
Huang, L., Shea, A. L., Qian, H., Masurkar, A., Deng, H., & Liu, D. (2019) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103291 · Cited by 437 · see citing works
Communication-Efficient Federated Learning for Wireless Edge Intelligence in IoT
Mills, J., Hu, J., & Min, G. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2956615 · Cited by 436 · see citing works
Federated Learning With Cooperating Devices: A Consensus Approach for Massive IoT Networks
Savazzi, S., Nicoli, M., & Rampa, V. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2964162 · Cited by 431 · see citing works
Lu, Y., Huang, X., Dai, Y., Maharjan, S., & Zhang, Y. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2942179 · Cited by 387 · see citing works
Federated Learning in Vehicular Edge Computing: A Selective Model Aggregation Approach
Ye, D., Yu, R., Pan, M., & Han, Z. (2020) · IEEE Access · doi:10.1109/access.2020.2968399 · Cited by 381 · see citing works
Dynamic-Fusion-Based Federated Learning for COVID-19 Detection
Zhang, W., Zhou, T., Lu, Q., Wang, X., Zhu, C., Sun, H., Wang, Z., Lo, S. K., & Wang, F.-Y. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3056185 · Cited by 329 · see citing works
Self-citationXu, R., Baracaldo, N., Zhou, Y., Anwar, A., & Ludwig, H. (2019) · ACM Workshop on Artificial Intelligence and Security (AISec) · doi:10.1145/3338501.3357371 · Cited by 328 · see citing works
Chained Anomaly Detection Models for Federated Learning: An Intrusion Detection Case Study
Preuveneers, D., Rimmer, V., Tsingenopoulos, I., Spooren, J., Joosen, W., & Ilie-Zudor, E. (2018) · Applied Sciences · doi:10.3390/app8122663 · Cited by 323 · see citing works
A Crowdsourcing Framework for On-Device Federated Learning
Pandey, S. R., Tran, N. H., Bennis, M., Tun, Y. K., Manzoor, A., & Hong, C. S. (2020) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2020.2971981 · Cited by 318 · see citing works
Blockchain-Based Federated Learning for Device Failure Detection in Industrial IoT
Zhang, W., Lu, Q., Yu, Q., Li, Z., Liu, Y., Lo, S. K., Chen, S., Xu, X., & Zhu, L. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3032544 · Cited by 306 · see citing works
Self-citationMulti-Objective Evolutionary Federated Learning
Zhu, H., & Jin, Y. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2919699 · Cited by 304 · see citing works
Zhang, X., Peng, M., Yan, S., & Sun, Y. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2962715 · Cited by 282 · see citing works
Liu, B., Wang, L., & Liu, M. (2019) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2019.2931179 · Cited by 242 · see citing works
Device Scheduling with Fast Convergence for Wireless Federated Learning
Shi, W., Zhou, S., & Niu, Z. (2020) · ICC 2020 - 2020 IEEE International Conference on Communications (ICC) · doi:10.1109/icc40277.2020.9149138 · Cited by 216 · see citing works
Federated Learning with Bayesian Differential Privacy
Triastcyn, A., & Faltings, B. V. (2019) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata47090.2019.9005465 · Cited by 213 · see citing works
Motivating Workers in Federated Learning: A Stackelberg Game Perspective
Sarikaya, Y., & Erçetin, Ö. (2019) · IEEE Networking Letters · doi:10.1109/lnet.2019.2947144 · Cited by 209 · see citing works
Multi-Task Network Anomaly Detection using Federated Learning
Zhao, Y., Chen, J., Wu, D., Teng, J., & Yu, S. (2019) · International Symposium on Information and Communication Technology · doi:10.1145/3368926.3369705 · Cited by 186 · see citing works
Ren, J., Wang, H., Hou, T., Zheng, S., & Tang, C. (2019) · IEEE Access · doi:10.1109/access.2019.2919736 · Cited by 185 · see citing works
How does Machine Learning Change Software Development Practices?
Wan, Z., Xia, X., Lo, D., & Murphy, G. C. (2020) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2019.2937083 · Cited by 181 · see citing works
FDC: A Secure Federated Deep Learning Mechanism for Data Collaborations in the Internet of Things
Yin, B., Yin, H., Wu, Y., & Jiang, Z. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2966778 · Cited by 149 · see citing works
Federated learning-based cognitive detection of jamming attack in flying Ad-Hoc network
Mowla, N. I., Tran, N. H., Doh, I., & Chae, K. (2020) · IEEE Access · doi:10.1109/access.2019.2962873 · Cited by 147 · see citing works
Awan, S., Li, F., Luo, B., & Liu, M. (2019) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3319535.3363256 · Cited by 125 · see citing works
DeepPAR and DeepDPA: Privacy Preserving and Asynchronous Deep Learning for Industrial IoT
Zhang, X., Chen, X., Liu, J. K., & Xiang, Y. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2941244 · Cited by 116 · see citing works
Anh, T. T., Luong, N. C., Niyato, D. T., Kim, D. I., & Wang, L. (2019) · IEEE Wireless Communications Letters · doi:10.1109/lwc.2019.2917133 · Cited by 114 · see citing works
Federated Echo State Learning for Minimizing Breaks in Presence in Wireless Virtual Reality Networks
Chen, M., Semiari, O., Saad, W., Liu, X., & Yin, C. (2019) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2019.2942929 · Cited by 112 · see citing works
Zhou, P., Wang, K., Guo, L., Gong, S., & Zheng, B. (2019) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2019.2936565 · Cited by 99 · see citing works
Performance Optimization of Federated Learning over Wireless Networks
Chen, M., Yang, Z., Saad, W., Yin, C., Poor, H. V., & Cui, S. R. (2019) · Globecom · doi:10.1109/globecom38437.2019.9013160 · Cited by 95 · see citing works
Mandal, K., & Gong, G. (2019) · Cloud Computing Security Workshop · doi:10.1145/3338466.3358926 · Cited by 91 · see citing works
Federated Learning with Autotuned Communication-Efficient Secure Aggregation
Bonawitz, K., Salehi, F., Konečný, J., McMahan, H. B., & Gruteser, M. (2019) · Asilomar Conference on Signals, Systems and Computers (ACSSC) · doi:10.1109/ieeeconf44664.2019.9049066 · Cited by 83 · see citing works
A Simple and Efficient Federated Recommender System
Jalalirad, A., Scavuzzo, M., Capota, C., & Sprague, M. (2019) · IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT) · doi:10.1145/3365109.3368788 · Cited by 75 · see citing works
Xu, M., Qian, F., Mei, Q., Huang, K. R., & Liu, X. (2018) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3287075 · Cited by 74 · see citing works
Privacy-Preserving Tensor Factorization for Collaborative Health Data Analysis
Ma, J., Zhang, Q., Lou, J., Ho, J. C., Xiong, L., & Jiang, X. (2019) · Proceedings of the 28th ACM International Conference on Information and Knowledge Management · doi:10.1145/3357384.3357878 · Cited by 51 · see citing works
Jiang, D., Song, Y., Tong, Y., Wu, X., Zhao, W., Xu, Q., & Yang, Q. (2019) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3357384.3357909 · Cited by 42 · see citing works
Secure Architectures Implementing Trusted Coalitions for Blockchained Distributed Learning (TCLearn)
Lugan, S., Desbordes, P., Brion, E., Ramos Tormo, L. X., Legay, A., & Macq, B. (2019) · IEEE Access · doi:10.1109/access.2019.2959220 · Cited by 39 · see citing works
Exploring federated learning on battery-powered devices
Xu, Z., Li, L., & Zou, W. (2019) · ACM Turing Celebration Conference - China (ACM TURC) · doi:10.1145/3321408.3323080 · Cited by 37 · see citing works
An Efficient Federated Learning Scheme with Differential Privacy in Mobile Edge Computing
Zhang, J., Wang, J., Zhao, Y., & Chen, B. (2019) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-030-32388-2_46 · Cited by 37 · see citing works
Daga, H., Nicholson, P. K., Gavrilovska, A., & Lugones, D. F. (2019) · ACM Symposium on Cloud Computing (SoCC) · doi:10.1145/3357223.3362708 · Cited by 36 · see citing works
Decentralized Attention-based Personalized Human Mobility Prediction
Fan, Z., Song, X., Jiang, R., et al. (2019) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3369830 · Cited by 33 · see citing works
Approaches to address the data skew problem in federated learning
Verma, D. C., White, G., Julier, S., Pasteris, S., Chakraborty, S., & Cirincione, G. (2019) · Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications · doi:10.1117/12.2519621 · Cited by 31 · see citing works
Distributed Statistical Machine Learning in Adversarial Settings
Chen, Y., Su, L., & Xu, J. (2018) · ACM SIGMETRICS Performance Evaluation Review · doi:10.1145/3292040.3219655 · Cited by 22 · see citing works
Federated AI for the Enterprise: A Web Services Based Implementation
Verma, D. C., White, G., & de Mel, G. R. (2019) · IEEE International Conference on Web Services (ICWS) · doi:10.1109/icws.2019.00016 · Cited by 19 · see citing works
Model and Feature Aggregation Based Federated Learning for Multi-sensor Time Series Trend Following
Hu, Y. F., Sun, X., Chen, Y., et al. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20521-8_20 · Cited by 15 · see citing works
Sun, X., Bommert, A., Pfisterer, F., et al. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-29516-5_48 · Cited by 10 · see citing works
Distributed deep neural network training on edge devices
Benditkis, D., Keren, A., Mor-Yosef, L., Avidor, T., Shoham, N., & Israel, N. (2019) · ACM/IEEE Symposium on Edge Computing (SEC) · doi:10.1145/3318216.3363324 · Cited by 9 · see citing works
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Self-citation
- Authors
- Lo, S. K., Lu, Q., Wang, C., Paik, H.-Y., & Zhu, L.
- Affiliations
- Commonwealth Scientific and Industrial Research Organisation (AU)
- UNSW Sydney (AU)
- Venue
- ACM Computing Surveys
- Published
- 2021-05-25
- Volume
- 54
- Issue
- 5
- Pages
- 1-39
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W3165798576
- Cited by
- 202
- References
- 65
- Citations by year
- 2026: 14 · 2025: 23 · 2024: 69 · 2023: 38 · 2022: 38 · 2021: 18 · 2020: 2
- FWCI
- 2.94
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- ACM Computing Surveys (publishedVersion)
- arXiv (Cornell University) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
- Physical Sciences › Computer Science › Artificial Intelligence › Stochastic Gradient Optimization Techniques
- Keywords
- federated learning, system development life cycle, architectural design, requirements analysis, system implementation, system evaluation, distributed learning
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