Federated Learning

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A cautionary tale on the cost-effectiveness of collaborative AI in real-world medical applications
Dec 9, 2024Francesco Cremonesi, Lucia Innocenti, Sebastien Ourselin +3Federated Learning Danger Recognition
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Oct 15, 2024Yawen Li, Yan Li, Junping Du +3Privacy-Preserving Framework Fedavg
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Class-wise Contribution Estimation via Logit Maximization for Federated Learning
Date pendingAsim Ukaye, Nurbek Tastan, Mubarak Abdu-Aguye +1Federated Learning Logit Lens
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Dec 9, 2024 · cs.LGJ/K move · Enter open · S save

A cautionary tale on the cost-effectiveness of collaborative AI in real-world medical applications

Francesco Cremonesi, Lucia Innocenti, Sebastien Ourselin, Vicky Goh+2

Epione Research Group, Inria Center of Universit´e Cˆote d’Azur, France · King’s College London, London, UK

Federated learning (FL) has gained wide popularity as a collaborative learning paradigm allowing collaborative Artificial Intelligence (AI) in sensitive healthcare applications. Nevertheless, 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 alternative for collaborative learning, allowing the combination of local knowledge into a federated decision system, while potentially reducing deployment overhead. Nevertheless, a comprehensive assessment of the viability of consensus-based learning as a cost-effective and sustainable alternative to federated learning has not yet been conducted. In this work we propose an extensive benchmark of the accuracy, cost-effectiveness, and sustainability of a panel of FL and CBL methods in a wide range of collaborative medical data analysis scenarios. The benchmark includes 7 different medical datasets, encompassing 3 machine learning tasks, 8 different data modalities, and multi-centric settings involving 3 to 23 clients. Our results reveal that CBL is a cost-effective and more sustainable alternative to FL. When compared across the panel of medical datasets in the considered benchmark, CBL methods provide equivalent accuracy to the one achieved by FL. Nonetheless, CBL significantly reduces training time and communication cost (resp. 15 fold and 131 fold decrease) (p-value < 0.05). 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 democratization of AI by alleviating the need for extensive computational resources.

Federated Learning Danger Recognition Real-World

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