Privacy-preserving horizontal federated learning methodology through a novel boosting-based federated random forest algorithm

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MertGencturk_PhDThesis_final.pdf

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2023-1-04

Author

Gençtürk, Mert

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In this thesis, a novel federated ensemble classification algorithm for horizontally partitioned data called Boosting-based Federated Random Forest (BOFRF) is proposed, which not only increases the predictive power of all participating sites, but also provides significantly high improvement on the predictive power of sites having unsuccessful local models. In this regard, a federated version of random forest, which is a well-known bagging algorithm, is implemented by adapting the idea of boosting to it. In the integration step, a novel aggregation and weight calculation methodology is introduced that assigns weights to local classifiers based on their classification performance at each site instead of proportioning them with the sample size or site index without increasing the communication or computation cost. To increase the predictive power of the federated models built through the proposed algorithm, a personalized implementation is presented where each participant fine-tunes the hyperparameters of BOFRF locally and come up with a better-performing federated model on their own datasets. In addition, a clustered extension is proposed where participants are clustered according to their data distribution similarities or differences prior to running the algorithm. Finally, to prevent security breaches from happening and increase the level of privacy, two different implementations are proposed for BOFRF, which are centralized implementation with a trusted third party and decentralized implementation using secure sum protocol. The performance of the proposed solution was evaluated in different federated environments that were set up by using four healthcare datasets. The empirical results show that the BOFRF algorithm and its extensions improve the predictive power of local random forest models in all cases. The advantage of the proposed methodology is that the level of improvement it provides for sites having unsuccessful local models is significantly high unlike existing solutions.

Subject Keywords

Federated learning, Ensemble learning, Machine learning, Random Forest classification, Privacy-preservation

URI

https://hdl.handle.net/11511/101867

Collections

Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

M. Gençtürk, “Privacy-preserving horizontal federated learning methodology through a novel boosting-based federated random forest algorithm,” Ph.D. - Doctoral Program, Middle East Technical University, 2023.