Privacy-Preserving Self-Taught Federated Learning for Heterogeneous Data

by   Kai-Fung Chu, et al.

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.


page 1

page 2

page 3

page 4


Data Valuation for Vertical Federated Learning: An Information-Theoretic Approach

Federated learning (FL) is a promising machine learning paradigm that en...

Federated Learning on Heterogeneous and Long-Tailed Data via Classifier Re-Training with Federated Features

Federated learning (FL) provides a privacy-preserving solution for distr...

FedV: Privacy-Preserving Federated Learning over Vertically Partitioned Data

Federated learning (FL) has been proposed to allow collaborative trainin...

Multi-Participant Multi-Class Vertical Federated Learning

Federated learning (FL) is a privacy-preserving paradigm for training co...

How global observation works in Federated Learning: Integrating vertical training into Horizontal Federated Learning

Federated learning (FL) has recently emerged as a transformative paradig...

CXR-FL: Deep Learning-based Chest X-ray Image Analysis Using Federated Learning

Federated learning enables building a shared model from multicentre data...

Towards Practical Watermark for Deep Neural Networks in Federated Learning

With the wide application of deep neural networks, it is important to ve...