Federated Learning: Centralized and P2P for a Siamese Deep Learning Model for Diabetes Foot Ulcer Classification
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Published:2023-11-28
Issue:23
Volume:13
Page:12776
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ISSN:2076-3417
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Container-title:Applied Sciences
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language:en
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Short-container-title:Applied Sciences
Author:
Toofanee Mohammud Shaad Ally12ORCID, Hamroun Mohamed13ORCID, Dowlut Sabeena2, Tamine Karim1, Petit Vincent2, Duong Anh Kiet4, Sauveron Damien1ORCID
Affiliation:
1. Department of Computer Science, XLIM, UMR CNRS 7252, University of Limoges, Avenue Albert Thomas, 87060 Limoges, France 2. Department of Applied Computer Science, Université des Mascareignes, Avenue de la Concorde, Roches Brunesl-Rose Hill 71259, Mauritius 3. 3iL Ingénieurs, 43 Rue de Sainte Anne, 87015 Limoges, France 4. Faculty of Science and Technology, University of Limoges, 23 Avenue Albert Thomas, 87060 Limoges, France
Abstract
It is a known fact that AI models need massive amounts of data for training. In the medical field, the data are not necessarily available at a single site but are distributed over several sites. In the field of medical data sharing, particularly among healthcare institutions, the need to maintain the confidentiality of sensitive information often restricts the comprehensive utilization of real-world data in machine learning. To address this challenge, our study experiments with an innovative approach using federated learning to enable collaborative model training without compromising data confidentiality and privacy. We present an adaptation of the federated averaging algorithm, a predominant centralized learning algorithm, to a peer-to-peer federated learning environment. This adaptation led to the development of two extended algorithms: Federated Averaging Peer-to-Peer and Federated Stochastic Gradient Descent Peer-to-Peer. These algorithms were applied to train deep neural network models for the detection and monitoring of diabetic foot ulcers, a critical health condition among diabetic patients. This study compares the performance of Federated Averaging Peer-to-Peer and Federated Stochastic Gradient Descent Peer-to-Peer with their centralized counterparts in terms of model convergence and communication costs. Additionally, we explore enhancements to these algorithms using targeted heuristics based on client identities and f1-scores for each class. The results indicate that models utilizing peer-to-peer federated averaging achieve a level of convergence that is comparable to that of models trained via conventional centralized federated learning approaches. This represents a notable progression in the field of ensuring the confidentiality and privacy of medical data for training machine learning models.
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
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