Abstract
AbstractFederated learning (FL) has gained wide popularity as a collaborative learning paradigm enabling trustworthy AI in sensitive healthcare applications. Never-theless, the practical implementation of FL presents technical and organizational challenges, as it generally requires complex communication infrastructures. In this context, consensus-based learning (CBL) may represent a promising collaborative learning alternative, thanks to the ability of combining local knowledge into a federated decision system, while potentially reducing deployment over-head. In this work we propose an extensive benchmark of the accuracy and cost-effectiveness of a panel of FL and CBL methods in a wide range of collaborative medical data analysis scenarios. Our results reveal that CBL is a cost-effective alternative to FL, providing comparable accuracy and significantly reducing training and communication costs. This study opens a novel perspective on the deployment of collaborative AI in real-world applications, whereas the adoption of cost-effective methods is instrumental to achieve sustainability and democratisation of AI by alleviating the need for extensive computational resources.
Publisher
Cold Spring Harbor Laboratory
Reference106 articles.
1. Collaborative learning without sharing data. Nature Machine Intelligence 3(459) (2021)
2. Key challenges for delivering clinical impact with artificial intelligence;BMC medicine,2019
3. Generalizability challenges of mortality risk prediction models: A retrospective analysis on a multi-center database;PLOS Digital Health,2022
4. Toward fairness in artificial intelligence for medical image analysis: identification and mitigation of potential biases in the roadmap from data collection to model deployment;Journal of Medical Imaging,2023
5. Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis