Abstract
We propose a privacy-preserving framework using Mondrian k-anonymity with decision trees in a Federated Learning (FL) setting for the horizontally partitioned data. Data heterogeneity in FL makes the data non-IID (Non-Independent and Identically Distributed). We use a novel approach to create non-IID partitions of data by solving an optimization problem. In this work, each device trains a decision tree classifier. Devices share the root node of their trees with the aggregator. The aggregator merges the trees by choosing the most common split attribute and grows the branches based on the split values of the chosen split attribute. This recursive process stops when all the nodes to be merged are leaf nodes. After the merging operation, the aggregator sends the merged decision tree to the distributed devices. Therefore, we aim to build a joint machine learning model based on the data from multiple devices while offering k-anonymity to the participants.
This study was partially funded by the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation.
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Kwatra, S., Torra, V. (2022). A k-Anonymised Federated Learning Framework with Decision Trees. In: Garcia-Alfaro, J., Muñoz-Tapia, J.L., Navarro-Arribas, G., Soriano, M. (eds) Data Privacy Management, Cryptocurrencies and Blockchain Technology. DPM CBT 2021 2021. Lecture Notes in Computer Science(), vol 13140. Springer, Cham. https://doi.org/10.1007/978-3-030-93944-1_7
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