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Abstract:
This work proposes FedSurvBoost, a federated learning pipeline for survival analysis based on the AdaBoost.F algorithm, which iteratively aggregates the best local weak hypotheses. Our method extends AdaBoost.F by removing the dependence on the number of classes coefficient from the computation of the weights of the best model. This makes it suitable for regression tasks, such as survival analysis. We show the effectiveness of our approach by comparing it with state-of-the-art methods, specifically developed for survival analysis problems, on two common survival datasets. Our code is available at https://github.com/oussamaHarrak/FedSurvBoost .
Tipologia CRIS:
04A-Conference paper in volume
Elenco autori:
Oussama Harrak; Bruno Casella; Samuele Fonio; Piero Fariselli; Gianluca Mittone; Cesare Rollo; Tiziana Sanavia; Marco Aldinucci
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Titolo del libro:
Machine Learning and Principles and Practice of Knowledge Discovery in Databases: International Workshops of ECML PKDD 2024
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