Federated Survival Forests
Alberto Archetti, Matteo Matteucci

TL;DR
This paper introduces FedSurF, a federated survival analysis algorithm based on random survival forests, achieving high discriminative power with minimal communication rounds while handling real-world data challenges.
Contribution
The paper presents FedSurF, a novel federated survival analysis method that combines the advantages of random forests with federated learning, requiring only one communication round.
Findings
FedSurF achieves comparable discriminative power to deep learning models with fewer communication rounds.
FedSurF outperforms state-of-the-art federated survival models in non-i.i.d. data environments.
FedSurF maintains low computational cost and handles missing data effectively.
Abstract
Survival analysis is a subfield of statistics concerned with modeling the occurrence time of a particular event of interest for a population. Survival analysis found widespread applications in healthcare, engineering, and social sciences. However, real-world applications involve survival datasets that are distributed, incomplete, censored, and confidential. In this context, federated learning can tremendously improve the performance of survival analysis applications. Federated learning provides a set of privacy-preserving techniques to jointly train machine learning models on multiple datasets without compromising user privacy, leading to a better generalization performance. However, despite the widespread development of federated learning in recent AI research, few studies focus on federated survival analysis. In this work, we present a novel federated algorithm for survival analysis…
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Health disparities and outcomes · Dementia and Cognitive Impairment Research
