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Decentralized Federated Learning Strategy with Image Classification using ResNet Architecture
conference contribution
posted on 2023-04-05, 06:18 authored by H Du, Srikanth ThudumuSrikanth Thudumu, S Singh, Scott BarnettScott Barnett, Rena LogothetisRena Logothetis, Rajesh VasaRajesh Vasa, Kon MouzakisKon MouzakisThe rapid growth of both the Industrial Internet of Things (IIoT) and Artificial Intelligence (AI) results in a high demand for AI applications in devices. To achieve high levels of accuracy, AI applications typically require a large amount of annotated data. Accessing such data is challenging in various applications such as healthcare, finance and information security. Federated learning (FL) is one of the strategies that was proposed to overcome this challenge. Specifically, FL enables the AI model in the centralized system to be trained without any prior knowledge of the information on the devices. Recent FLs have the disadvantage that they are dependent upon a centralized system, and thus are susceptible to single points of failure. This paper proposes a strategy that employs FL in a decentralized environment where devices can communicate with each other to increase the accuracy of the AI model in each device. Furthermore, we evaluate the proposed strategy in the image classification task with the ResNet50 architecture and the CIFAR-10 dataset. The evaluation shows that the ResNet50 model trained in the decentralized environment can achieve comparable results to the model trained in the centralized environment.
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Volume
2023-JanuaryPagination
706-707Location
Las Vegas, Nev.Publisher DOI
Start date
2023-01-08End date
2023-01-11ISSN
2331-9860eISSN
2331-9860ISBN-13
9781665497343Language
EnglishTitle of proceedings
Proceedings - IEEE Consumer Communications and Networking Conference, CCNCEvent
2023 IEEE 20th Consumer Communications & Networking Conference (CCNC)Publisher
IEEEPlace of publication
Piscataway, N.J.Usage metrics
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