Abstract
Edge computing is a paradigm that involves performing local processing on lightweight devices at the edge of networks to improve response times and reduce bandwidth consumption. While machine learning (ML) models can run on smaller computing devices at the edge, training ML models presents challenges for low-capacity devices. This paper aimed to evaluate the performance of Federated Learning (FL) - a distributed ML framework, when training a medical dataset using Raspberry Pi devices as client nodes. The testing accuracy, CPU usage, RAM memory usage and network performance were measured for different number of clients and epochs. The results showed that increasing the number of devices generally improved the testing accuracy, with the greatest improvement observed in the earlier epochs. However, increasing the number of devices also increased the CPU usage, with a significant increase observed in the later epochs. Additionally, the RAM memory usage increased slightly as the number of clients and epochs increased. The findings suggest that FL can be an effective way to train medical models using distributed devices, but careful consideration must be given to the trade-off between accuracy and computational resources.
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References
Satyanarayanan, M.: The emergence of edge computing. Computer 50(1), 30–39 (2017)
Selimi, M., Lertsinsrubtavee, A., Sathiaseelan, A., Cerdà-Alabern, L., Navarro, L.: Picasso: enabling information-centric multi-tenancy at the edge of community mesh networks. Comput. Netw. 164, 106897 (2019)
Sakr, F., Bellotti, F., Berta, R., De Gloria, A.: Machine learning on mainstream microcontrollers. Sensors 20(9), 2638 (2020)
Arikumar, K.S., et al.: FL-PMI: federated learning-based person movement identification through wearable devices in smart healthcare systems. Sensors 22(4), 1377 (2022)
Farhad, A., Woolley, S., Andras, P.: Federated learning for AI to improve patient care using wearable and IoMT sensors. In: 2021 IEEE 9th International Conference on Healthcare Informatics (ICHI), p. 434 (2021)
Yang, Q., Liu, Y., Chen, T., Tong, Y.: Federated machine learning: concept and applications. ACM Trans. Intell. Syst. Technol. 10(2), 1–19 (2019)
Yang, K., Jiang, T., Shi, Y., Ding, Z.: Federated learning via over-the-air computation. IEEE Trans. Wireless Commun. 19(3), 2022–2035 (2020)
Pinyoanuntapong, P., Janakaraj, P., Wang, P., Lee, M., Chen, C.: Fedair: towards multi-hop federated learning over-the-air. In: 2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), pp. 1–5 (2020)
Freitag, F., Vilchez, P., Wei, L., Liu, C.H., Selimi, M.: Performance evaluation of federated learning over wireless mesh networks with low-capacity devices. In: Rocha, Á., Ferrás, C., Méndez Porras, A., Jimenez Delgado, E. (eds.) ICITS 2022, pp. 635–645. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-96293-7_53
Women, G., Center, C.M.: Chest X-ray images (pneumonia). https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia. Accessed 28 Apr 2021
Gao, Y., et al.: End-to-end evaluation of federated learning and split learning for internet of things. In: 2020 International Symposium on Reliable Distributed Systems (SRDS), pp. 91–100 (2020)
Abreha, H.G., Hayajneh, M., Serhani, M.A.: Federated learning in edge computing: a systematic survey. Sensors 22(2), 450 (2022)
Mathur, A., et al.: On-device federated learning with flower (2021)
Cetinkaya, A.E., Akin, M., Sagiroglu, S.: A communication efficient federated learning approach to multi chest diseases classification. In: 2021 6th International Conference on Computer Science and Engineering (UBMK), pp. 429–434 (2021)
Hakak, S., Ray, S., Khan, W.Z., Scheme, E.: A framework for edge-assisted healthcare data analytics using federated learning. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 3423–3427 (2020)
Malekzadeh, M., Hasircioglu, B., Mital, N., Katarya, K., Ozfatura, M.E., Gunduz, D.: Dopamine: differentially private federated learning on medical data. arXiv abs/2101.11693 (2021)
Lu, Y., Huang, X., Dai, Y., Maharjan, S., Zhang, Y.: Blockchain and federated learning for privacy-preserved data sharing in industrial IoT. IEEE Trans. Industr. Inf. 16(6), 4177–4186 (2020)
Kim, H., Park, J., Bennis, M., Kim, S.: On-device federated learning via blockchain and its latency analysis. CoRR abs/1808.03949 (2018)
Passerat-Palmbach, J., Farnan, T., Miller, R., Gross, M.S., Flannery, H.L., Gleim, B.: A blockchain-orchestrated federated learning architecture for healthcare consortia. CoRR abs/1910.12603 (2019)
Pappas, C., Chatzopoulos, D., Lalis, S., Vavalis, M.: IPLS: a framework for decentralized federated learning (2021)
Mills, J., Hu, J., Min, G.: Communication-efficient federated learning for wireless edge intelligence in IoT. IEEE Internet Things J. 7(7), 5986–5994 (2020)
Luo, J., Wu, S.: FedSLD: federated learning with shared label distribution for medical image classification. In: 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), pp. 1–5 (2022)
Niknam, S., Dhillon, H.S., Reed, J.H.: Federated learning for wireless communications: motivation, opportunities, and challenges. IEEE Commun. Mag. 58(6), 46–51 (2020)
Ibraimi, L., Selimi, M., Freitag, F.: Bepoch: improving federated learning performance in resource-constrained computing devices. In: IEEE Global Communications Conference (GLOBECOM) (2021)
Kermany, D.S., et al.: Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172(5), 1122–1131.e9 (2018)
Acknowledgment
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 872614 - SMART4ALL. SMART4ALL is a four-year Innovation Action project funded under call DT-ICT-01-2019: Smart Anything Everywhere - Area 2: Customized low energy computing powering CPS and the IoT. The authors wish to express their gratitude to the SMART4All consortium partners for their valuable comments and feedback, which have contributed to the enhancement of this work.
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Shkurti, L., Selimi, M., Besimi, A. (2024). FederatedMesh: Collaborative Federated Learning for Medical Data Sharing in Mesh Networks. In: Gao, H., Wang, X., Voros, N. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 563. Springer, Cham. https://doi.org/10.1007/978-3-031-54531-3_9
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