| Texto completo | |
| Autor(es): |
de Aguiar, Erikson J.
;
Traina, Agma J. M.
;
Helal, Sumi
Número total de Autores: 3
|
| Tipo de documento: | Artigo Científico |
| Fonte: | 2024 IEEE INTERNATIONAL CONFERENCE ON BIG DATA, BIGDATA; v. N/A, p. 10-pg., 2024-01-01. |
| Resumo | |
Traditional Deep Learning (DL) approaches for medical image classification rely on centralized, static datasets, which do not adequately reflect the dynamic, real-world medical practice where data is continually generated. In contrast, Federated Learning (FL) enables decentralized model training on localized data while preserving privacy. Yet, current FL methods struggle to handle Non-Identically Independently Distributed (Non-IID) data streams over time. This paper introduces MedTimeSplit, a novel dataset partitioning strategy that integrates Online Continual Learning (OCL) with FL to simulate real-world medical data flows more realistically and effectively. MedTimeSplit partitions data into Non-IID, time-based increments, mimicking dynamic sourcing in medical environments. We evaluate its impact on FL model performance for medical image classification, focusing on skin lesions, and analyze the system's resilience to backdoor attacks. Our experiments demonstrate that MedTimeSplit outperforms existing methods in both accuracy and robustness, offering a viable solution for real-world medical applications. Additionally, we propose new metrics to measure model behavior over time, including Average Bad Decisions (ABD) and Overall Changing Mistakes (OCM), which provide deeper insights into model performance specifically under OCL conditions. The results highlight the promise of combining OCL with FL in the medical domain, paving the way for more secure and adaptive healthcare solutions. The source code is available on GitHub(1). (AU) | |
| Processo FAPESP: | 24/13328-9 - Gestão inteligente de dados multimodais de saúde para tomada de decisão em cenários de big data: IHealth-MD |
| Beneficiário: | Agma Juci Machado Traina |
| Modalidade de apoio: | Auxílio à Pesquisa - Temático |
| Processo FAPESP: | 16/17078-0 - Mineração, indexação e visualização de Big Data no contexto de sistemas de apoio à decisão clínica (MIVisBD) |
| Beneficiário: | Agma Juci Machado Traina |
| Modalidade de apoio: | Auxílio à Pesquisa - Temático |
| Processo FAPESP: | 23/18026-8 - Centro de Ciência de Dados para Estatísticas Públicas (CCDEP) |
| Beneficiário: | Carlos Eduardo Torres Freire |
| Modalidade de apoio: | Auxílio à Pesquisa - Centros de Ciência para o Desenvolvimento |
| Processo FAPESP: | 23/14759-0 - Preservação da privacidade e defesa de backdoors: rumo à aprendizagem federada em contextos médicos |
| Beneficiário: | Erikson Júlio de Aguiar |
| Modalidade de apoio: | Bolsas no Exterior - Estágio de Pesquisa - Doutorado |