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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Interplanetary File System. https://ipfs.io/.
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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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