Privacy-Preserving Self-Taught Federated Learning for Heterogeneous Data

TL;DR

This paper introduces a privacy-preserving self-taught federated learning approach for heterogeneous data that enhances training speed, flexibility, and data privacy in vertical federated learning scenarios.

Contribution

It proposes a novel self-taught federated learning method using unsupervised feature extraction, addressing limitations of existing vertical FL methods like neural network restrictions and slow training.

Findings

Improved training speed compared to traditional methods

Enhanced privacy by local storage of sensitive data and parameters

Effective handling of unmatched ID data in federated settings

Abstract

Many application scenarios call for training a machine learning model among multiple participants. Federated learning (FL) was proposed to enable joint training of a deep learning model using the local data in each party without revealing the data to others. Among various types of FL methods, vertical FL is a category to handle data sources with the same ID space and different feature spaces. However, existing vertical FL methods suffer from limitations such as restrictive neural network structure, slow training speed, and often lack the ability to take advantage of data with unmatched IDs. In this work, we propose an FL method called self-taught federated learning to address the aforementioned issues, which uses unsupervised feature extraction techniques for distributed supervised deep learning tasks. In this method, only latent variables are transmitted to other parties for model training, while privacy is preserved by storing the data and parameters of activations, weights, and biases locally. Extensive experiments are performed to evaluate and demonstrate the validity and efficiency of the proposed method.