End-to-End Evaluation of Federated Learning and Split Learning for Internet of Things
Open accessAbstract
Federated learning (FL) and split neural networks (SplitNN) are state-of-art distributed machine learning techniques to enable machine learning without directly accessing raw data on clients or end devices. In theory, such distributed machine learning techniques have great potential in distributed applications, in which data are typically generated and collected at the client-side while the collected data should be processed by the application deployed at the server-side. However, there is still a significant gap in evaluating the performance of those techniques concerning their practicality in the Internet of Things (IoT)-enabled distributed systems constituted by resource-constrained devices. This work is the first attempt to provide empirical comparisons of FL and SplitNN in real-world IoT settings in terms of learning performance and device implementation overhead. We consider a variety of datasets, different model architectures, multiple clients, and various performance metrics. For the learning performance (i.e., model accuracy and convergence time), we empirically evaluate both FL and SplitNN under different types of data distributions such as imbalanced and non-independent and identically distributed (non-IID) data. We show that the learning performance of SplitNN is better than FL under an imbalanced data distribution but worse than FL under an extreme non-IID data distribution. For implementation overhead, we mount both FL and SplitNN on Raspberry Pi devices and comprehensively evaluate their overhead, including training time, communication overhead, power consumption, and memory usage. Our key observations are that under the IoT scenario where the communication traffic is the primary concern, FL appears to perform better over SplitNN because FL has a significantly lower communication overhead compared with SplitNN. However, our experimental results also demonstrate that neither FL or SplitNN can be applied to a heavy model, e.g., with several million parameters, on resource-constrained IoT devices because its training cost would be too expensive for such devices. Source code is released and available: https://github.com/Minki-Kim95/Federated-Learning-and-Split-Learning-with-raspberry-pi.
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Pruning can be an effective defense against backdoor attacks.
“Plus, pruning can be an effective defense against backdoor attacks [66].”
From Vulnerabilities in federated learning (2021) · cites this paper as [66]
Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., & Adam, H. (2017) · arXiv · doi:10.48550/arxiv.1704.04861 · Cited by 19,015 · see citing works
Communication-Efficient Learning of Deep Networks from Decentralized Data
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. Y. (2016) · arXiv · doi:10.48550/arxiv.1602.05629 · Cited by 14,215 · see citing works
Distilling the Knowledge in a Neural Network
Hinton, G., Vinyals, O., & Dean, J. (2015) · arXiv · doi:10.48550/arxiv.1503.02531 · Cited by 14,204 · 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
The impact of the MIT-BIH Arrhythmia Database
Moody, G. B., & Mark, R. G. (2001) · IEEE Engineering in Medicine and Biology Magazine · doi:10.1109/51.932724 · Cited by 4,834 · see citing works
XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks
Rastegari, M., Ordonez, V., Redmon, J., & Farhadi, A. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_32 · Cited by 4,408 · see citing works
Towards Federated Learning at Scale: System Design
Bonawitz, K., Eichner, H., Grieskamp, W., Huba, D., Ingerman, A., Ivanov, V., Kiddon, C., Konečný, J., Mazzocchi, S., McMahan, H. B., et al. (2019) · arXiv · doi:10.48550/arxiv.1902.01046 · Cited by 2,334 · see citing works
Federated Learning with Non-IID Data
Zhao, Y., Li, M., Lai, L., Suda, N., Civin, D., & Chandra, V. (2018) · arXiv · doi:10.48550/arxiv.1806.00582 · Cited by 2,077 · see citing works
Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks
Kıranyaz, S., İnce, T., & Gabbouj, M. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2468589 · Cited by 1,925 · see citing works
Deep Learning With Edge Computing: A Review
Chen, J., & Ran, X. (2019) · Proceedings of the IEEE · doi:10.1109/jproc.2019.2921977 · Cited by 1,540 · cited 1× · see citing works
“Indeed, as highlighted in [16], there is a demand to understand the deep learning performances on resource-constrained IoT/edge device hardware like Raspberry Pi [17].”
BackgroundDeep learning approach for Network Intrusion Detection in Software Defined Networking
Tang, T. A., Mhamdi, L., McLernon, D., Zaidi, S. A. R., & Ghogho, M. (2016) · International Conference on Wireless Networks and Mobile Communications (WINCOM) · doi:10.1109/wincom.2016.7777224 · Cited by 843 · see citing works
SplitFed: When Federated Learning Meets Split Learning
Thapa, C., Arachchige, P. C. M., Camtepe, S. A., & Sun, L. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i8.20825 · Cited by 744 · see citing works
Self-citationDistributed learning of deep neural network over multiple agents
Gupta, O., & Raskar, R. (2018) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2018.05.003 · Cited by 726 · see citing works
DÏoT: A Federated Self-learning Anomaly Detection System for IoT
Nguyen, T. D., Marchal, S., Miettinen, M., Fereidooni, H., Asokan, N., & Sadeghi, A.-R. (2019) · 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs.2019.00080 · Cited by 663 · 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
Classification of ECG signals based on 1D convolution neural network
Li, D. X., Zhang, J., Zhang, Q., & Wei, X. (2017) · IEEE International Conference on e-Health Networking, Applications and Services (Healthcom) · doi:10.1109/healthcom.2017.8210784 · Cited by 236 · see citing works
A Survey on Resource Management in IoT Operating Systems
Musaddiq, A., Zikria, Y. B., Hahm, O., Yu, H., Bashir, A. K., & Kim, S. W. (2018) · IEEE Access · doi:10.1109/access.2018.2808324 · Cited by 200 · see citing works
AdderNet: Do We Really Need Multiplications in Deep Learning?
Chen, H., Wang, Y., Xu, C., Shi, B., Xu, C., Tian, Q., & Xu, C. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00154 · Cited by 199 · see citing works
Adversary Resistant Deep Neural Networks with an Application to Malware Detection
Wang, Q., Guo, W., Zhang, K., Ororbia, A. G., Xing, X., Liu, X., & Giles, C. L. (2017) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3097983.3098158 · Cited by 177 · see citing works
Detailed comparison of communication efficiency of split learning and federated learning
Singh, A., Vepakomma, P., Gupta, O., & Raskar, R. (2019) · arXiv · doi:10.48550/arxiv.1909.09145 · Cited by 148 · see citing works
No Peek: A Survey of private distributed deep learningPreprint
Vepakomma, P., Swedish, T. B., Raskar, R., Gupta, O., & Dubey, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.03288 · Cited by 88 · see citing works
A survey of Algorithms and Analysis for Adaptive Online Learning
McMahan, H. B. (2017) · Journal of Machine Learning Research · doi:10.5555/3122009.3176834 · Cited by 87 · see citing works
Training wide residual networks for deployment using a single bit for each weightPreprint
McDonnell, M. D. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.08530 · Cited by 41 · see citing works
SplitFed: When Federated Learning Meets Split LearningPreprint
Thapa, C., Chamikara, M. A. P., Camtepe, S. A., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.12088 · Cited by 41 · see citing works
Self-citationA Survey of Algorithms and Analysis for Adaptive Online LearningPreprint
McMahan, H. B. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1403.3465 · Cited by 26 · see citing works
The Seven Sins of Personal-Data Processing Systems under GDPRPreprint
Shastri, S., Wasserman, M. F., & Chidambaram, V., V (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.09305 · Cited by 26 · see citing works
Federated Continual Learning with Adaptive Parameter Communication.Preprint
Yoon, J., Jeong, W., Lee, G., Yang, E., & Hwang, S. J. (2020) · arXiv (Cornell University) · Cited by 15 · see citing works
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Tan, A. Z., Yu, H., Cui, L., & Yang, Q. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3160699 · Cited by 1,366 · see citing works
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Thapa, C., Arachchige, P. C. M., Camtepe, S. A., & Sun, L. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i8.20825 · Cited by 744 · see citing works
Self-citationXu, W., Yang, Z., Ng, D. W. K., Levorato, M., Eldar, Y. C., & Debbah, M. A. (2023) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2023.3239189 · Cited by 681 · see citing works
Thapa, C., & Camtepe, S. A. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.104130 · Cited by 424 · see citing works
Self-citationFerrag, M. A., Friha, O., Maglaras, L., Janicke, H., & Shu, L. (2021) · IEEE Access · doi:10.1109/access.2021.3118642 · Cited by 342 · see citing works
Rahman, M. A., Hossain, M. S., Islam, M. S., Alrajeh, N. A., & Muhammad, G. (2020) · IEEE Access · doi:10.1109/access.2020.3037474 · Cited by 312 · see citing works
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Wu, W., Li, M.-S., Qu, K., Zhou, C.-H., Shen, X., Zhuang, W., Li, X., & Shi, W. (2022) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2023.3242704 · Cited by 298 · see citing works
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Li, D., Han, D., Weng, T., Zheng, Z., Li, H., Liu, H., Castiglione, A., & Li, K. (2021) · Soft Computing · doi:10.1007/s00500-021-06496-5 · Cited by 208 · see citing works
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Gao, Y., Doan, B. G., Zhang, Z., Ma, S., Zhang, J., Fu, A., Nepal, ., & Kim, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.10760 · Cited by 207 · see citing works
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Petersen, E., Potdevin, Y., Mohammadi, E., Zidowitz, S., Breyer, S., Nowotka, D., Henn, S., Pechmann, L., Leucker, M., Rostalski, P., et al. (2022) · IEEE Access · doi:10.1109/access.2022.3178382 · Cited by 83 · see citing works
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Wan, X., Zheng, Y., Li, Q., Fu, A., Su, M., & Gao, Y. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.109193 · Cited by 18 · see citing works
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Lin, D., Guo, Y., Sun, H., & Chen, Y. (2022) · IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) · doi:10.1109/infocomwkshps54753.2022.9797945 · Cited by 18 · see citing works
Exploring SVM for Federated Machine Learning Applications
Nair, D. G., Narayana, C. V. A., Reddy, K. J., & Nair, J. J. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-19-1018-0_25 · Cited by 19 · see citing works
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Park, S. J., & Ye, J. C. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3218783 · Cited by 18 · see citing works
Multi-Dimensional Resource Orchestration Toward Edge Intelligence in 6G Networks
Zhang, X., Han, P., Feng, C., Tianchun, M., & Guo, L. (2023) · IEEE Communications Magazine · doi:10.1109/mcom.005.2200905 · Cited by 18 · see citing works
An Efficient Privacy-Aware Split Learning Framework for Satellite Communications
Sun, J., Wu, C., Mumtaz, S., Tao, J., Cao, M., Wang, M., & Frascolla, V. (2024) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2024.3459027 · Cited by 18 · see citing works
An Efficient and Private ECG Classification System Using Split and Semi-Supervised Learning
Ayad, A., Barhoush, M., Frei, M., Völker, B., & Schmeink, A. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3281977 · Cited by 17 · see citing works
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Tajalli, B., Ersoy, O., & Picek, S. (2023) · IEEE Security and Privacy Workshops (SPW) · doi:10.1109/spw59333.2023.00014 · Cited by 17 · see citing works
Li, J., Liao, G., Chen, L., & Chen, X. (2023) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2023.3312304 · Cited by 17 · see citing works
EcoFed: Efficient Communication for DNN Partitioning-Based Federated Learning
Wu, D., Ullah, R., Rodgers, P., Kilpatrick, P., Spence, I. T. A., & Varghese, B. (2024) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2024.3349617 · Cited by 17 · see citing works
Mao, Y., Xin, Z., Li, Z., Hong, J., Yang, Q., & Zhong, S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-51482-1_2 · Cited by 20 · see citing works
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Park, S. J., Kim, G., Kim, J., Kim, B., & Ye, J. C. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.01338 · Cited by 17 · see citing works
A Review and Experimental Evaluation on Split Learning
Hu, Z., Zhou, T., Wu, B., Chen, C., & Wang, Y. (2025) · Future Internet · doi:10.3390/fi17020087 · Cited by 19 · see citing works
Pencil: Private and Extensible Collaborative Learning without the Non-Colluding Assumption
Liu, X., Liu, Z., Li, Q., Xu, K., & Xu, M. (2024) · Network and Distributed System Security Symposium · doi:10.14722/ndss.2024.24512 · Cited by 16 · see citing works
Chronic Poisoning: Backdoor Attack against Split Learning
Yu, F., Zeng, B., Zhao, K., Pang, Z., & Wang, L. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i15.29591 · Cited by 16 · see citing works
CRSFL: Cluster-based Resource-aware Split Federated Learning for Continuous Authentication
Wazzeh, M., Arafeh, M., Sami, H., Ould‐Slimane, H., Talhi, C., Mourad, A., & Otrok, H. (2024) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2024.103987 · Cited by 16 · see citing works
Efficient Model Training in Edge Networks With Hierarchical Split Learning
Zhang, S., Wu, W. R., Song, L., & Shen, X. (2025) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2025.3569407 · Cited by 16 · see citing works
Embedded Federated Learning for VANET Environments
Valente, R., Senna, C. R., Rito, P., & Sargento, S. (2023) · Applied Sciences · doi:10.3390/app13042329 · Cited by 15 · see citing works
FedComm: Understanding Communication Protocols for Edge-based Federated Learning
Cleland, G., Wu, D., Ullah, R., & Varghese, B. (2022) · IEEE/ACM International Conference on Utility and Cloud Computing (UCC) · doi:10.1109/ucc56403.2022.00018 · Cited by 15 · see citing works
Split Without a Leak: Reducing Privacy Leakage in Split Learning
Nguyen, K., Khan, T., & Michalas, A. (2024) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-64954-7_17 · Cited by 15 · see citing works
Efficient Distributed DNNs in the Mobile-Edge-Cloud Continuum
Malandrino, F., Chiasserini, C. F., & di Giacomo, G. (2022) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2022.3222640 · Cited by 14 · see citing works
IoTSL: Toward Efficient Distributed Learning for Resource-Constrained Internet of Things
Feng, X., Luo, C., Chen, J., Huang, Y., Zhang, J., Xu, W., Li, J., & Leung, V. C. M. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3235765 · Cited by 14 · see citing works
Security threats to agricultural artificial intelligence: Position and perspective
Gao, Y., Camtepe, S. A., Sultan, N. H., Bui, H. T., Mahboubi, A., Aboutorab, H., Bewong, M., Islam, R., Islam, M. Z., Chauhan, A., et al. (2024) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2024.109557 · Cited by 17 · see citing works
Self-citationEnabling efficient and low-effort decentralized federated learning with the EdgeFL framework
Zhang, H., Bosch, J., & Olsson, H. H. (2024) · Information and Software Technology · doi:10.1016/j.infsof.2024.107600 · Cited by 16 · see citing works
Vulnerability Due to Training Order in Split Learning
Madaan, H., Kulkarni, M. G. V., & Pant, A. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-16-5987-4_11 · Cited by 13 · see citing works
Efficient and Private ECG Classification on the Edge Using a Modified Split Learning Mechanism
Ayad, A., Frei, M., & Schmeink, A. (2022) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi54592.2022.00048 · Cited by 13 · see citing works
Low-Latency Federated Learning via Dynamic Model Partitioning for Healthcare IoT
He, P., Lan, C., Bashir, A. K., Wu, D., Wang, R., Kharel, R., & Yu, K. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3298446 · Cited by 12 · see citing works
Mix2SFL: Two-Way Mixup for Scalable, Accurate, and Communication-Efficient Split Federated Learning
Oh, S., Nam, H., Park, J., Vepakomma, P., Raskar, R., Bennis, M., & Kim, S. (2023) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2023.3328424 · Cited by 12 · see citing works
Socialized Learning: A Survey of the Paradigm Shift for Edge Intelligence in Networked Systems
Wang, X., Zhao, Y., Qiu, C., Hu, Q., & Leung, V. C. M. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3482978 · Cited by 12 · see citing works
Multi-Dimensional Training Optimization for Efficient Federated Synergy Learning
Fu, S., Dong, F., Chen, R., Shen, D., Zhang, J., & He, Q. (2025) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2025.3540566 · Cited by 12 · see citing works
Smart Meter Modbus RS-485 Intrusion Detection by Federated Learning Approach
Hossain, M. D., Ochiai, H., Khan, L. R., & Kadobayashi, Y. (2023) · International Conference on Computer and Automation Engineering · doi:10.1109/iccae56788.2023.10111132 · Cited by 12 · see citing works
On the Robustness of Split Learning Against Adversarial Attacks
Fan, M., Chen, C., Wang, C., Zhou, W., & Huang, J. (2023) · Frontiers in artificial intelligence and applications · doi:10.3233/faia230330 · Cited by 12 · see citing works
Review of Recent Technologies for Tackling COVID-19
Alharbi, A. A., & Rahman, M. A. (2021) · SN Computer Science · doi:10.1007/s42979-021-00841-z · Cited by 12 · see citing works
- Authors
- Gao, Y., Kim, M., Abuadbba, S., Kim, Y., Thapa, C., Kim, K., Camtep, S. A., Kim, H., & Nepal, S.
- Affiliations
- Commonwealth Scientific and Industrial Research Organisation (AU)
- Cyber Security Cooperative Research Centre,Australia
- Data61, CSIRO,Syndey,Australia
- Venue
- 2020 International Symposium on Reliable Distributed Systems (SRDS)
- Publisher
- IEEE
- Published
- 2020-09
- Pages
- 91-100
- ISSN
- 1060-9857 (ISSN-L)
- OpenAlex
- W3105915864
- Cited by
- 240
- References
- 28
- Citations by year
- 2026: 16 · 2025: 45 · 2024: 59 · 2023: 57 · 2022: 31 · 2021: 26 · 2020: 6
- FWCI
- 11.46
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Symposium on Reliable Distributed Systems (SRDS) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Australian Government
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Keywords
- federated learning, Internet of Things, resource-constrained devices, non-IID data, class imbalance, communication overhead, power consumption, memory usage, training duration, Raspberry Pi, distributed learning, end-to-end evaluation
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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