Model-Free-Communication Federated Learning: Framework and application to Precision Medicine

De Falco, I.; Della Cioppa, A.; Koutny, T.; Scafuri, U.; Tarantino, E.

doi:10.1016/j.bspc.2023.105416

Cited by 5 publications

(1 citation statement)

References 51 publications

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“…16. [160]. Collaborative efforts between researchers, healthcare providers, and technology developers are essential to overcome these challenges and realize the potential of federated learning in improving healthcare outcomes [161].…”

Section: Lack Of Standardizationmentioning

confidence: 99%

A Comprehensive Survey on Federated Learning in the Healthcare Area: Concept and Applications

Upreti,

Yang,

Kim

et al. 2024

CMES

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Federated learning is an innovative machine learning technique that deals with centralized data storage issues while maintaining privacy and security. It involves constructing machine learning models using datasets spread across several data centers, including medical facilities, clinical research facilities, Internet of Things devices, and even mobile devices. The main goal of federated learning is to improve robust models that benefit from the collective knowledge of these disparate datasets without centralizing sensitive information, reducing the risk of data loss, privacy breaches, or data exposure. The application of federated learning in the healthcare industry holds significant promise due to the wealth of data generated from various sources, such as patient records, medical imaging, wearable devices, and clinical research surveys. This research conducts a systematic evaluation and highlights essential issues for the selection and implementation of federated learning approaches in healthcare. It evaluates the effectiveness of federated learning strategies in the field of healthcare. It offers a systematic analysis of federated learning in the healthcare domain, encompassing the evaluation metrics employed. In addition, this study highlights the increasing interest in federated learning applications in healthcare among scholars and provides foundations for further studies.

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Section: Lack Of Standardizationmentioning

confidence: 99%

A Comprehensive Survey on Federated Learning in the Healthcare Area: Concept and Applications

Upreti,

Yang,

Kim

et al. 2024

CMES

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Federated Learning of XAI Models in Healthcare: A Case Study on Parkinson’s Disease

Ducange,

Marcelloni,

Renda

et al. 2024

Cogn Comput

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Artificial intelligence (AI) systems are increasingly used in healthcare applications, although some challenges have not been completely overcome to make them fully trustworthy and compliant with modern regulations and societal needs. First of all, sensitive health data, essential to train AI systems, are typically stored and managed in several separate medical centers and cannot be shared due to privacy constraints, thus hindering the use of all available information in learning models. Further, transparency and explainability of such systems are becoming increasingly urgent, especially at a time when “opaque” or “black-box” models are commonly used. Recently, technological and algorithmic solutions to these challenges have been investigated: on the one hand, federated learning (FL) has been proposed as a paradigm for collaborative model training among multiple parties without any disclosure of private raw data; on the other hand, research on eXplainable AI (XAI) aims to enhance the explainability of AI systems, either through interpretable by-design approaches or post-hoc explanation techniques. In this paper, we focus on a healthcare case study, namely predicting the progression of Parkinson’s disease, and assume that raw data originate from different medical centers and data collection for centralized training is precluded due to privacy limitations. We aim to investigate how FL of XAI models can allow achieving a good level of accuracy and trustworthiness. Cognitive and biologically inspired approaches are adopted in our analysis: FL of an interpretable by-design fuzzy rule-based system and FL of a neural network explained using a federated version of the SHAP post-hoc explanation technique. We analyze accuracy, interpretability, and explainability of the two approaches, also varying the degree of heterogeneity across several data distribution scenarios. Although the neural network is generally more accurate, the results show that the fuzzy rule-based system achieves competitive performance in the federated setting and presents desirable properties in terms of interpretability and transparency.

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Sirael: Virtual Metabolic Machine

Koutny

2024

Biomedical Materials & Devices

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Model-Free-Communication Federated Learning: Framework and application to Precision Medicine

Cited by 5 publications

References 51 publications

A Comprehensive Survey on Federated Learning in the Healthcare Area: Concept and Applications

A Comprehensive Survey on Federated Learning in the Healthcare Area: Concept and Applications

Federated Learning of XAI Models in Healthcare: A Case Study on Parkinson’s Disease

Sirael: Virtual Metabolic Machine

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