Histopathological Image Classification and Vulnerability Analysis using Federated Learning

TL;DR

This paper explores federated learning for histopathological image classification in healthcare, demonstrating its privacy benefits and vulnerability to data poisoning attacks, specifically label flipping, which reduces model accuracy.

Contribution

It introduces a privacy-preserving federated learning method for skin cancer classification and analyzes its susceptibility to data poisoning attacks like label flipping.

Findings

Federated learning protects privacy during model training.

Data poisoning via label flipping significantly reduces model accuracy.

Model optimization with stochastic gradient descent improves performance.

Abstract

Healthcare is one of the foremost applications of machine learning (ML). Traditionally, ML models are trained by central servers, which aggregate data from various distributed devices to forecast the results for newly generated data. This is a major concern as models can access sensitive user information, which raises privacy concerns. A federated learning (FL) approach can help address this issue: A global model sends its copy to all clients who train these copies, and the clients send the updates (weights) back to it. Over time, the global model improves and becomes more accurate. Data privacy is protected during training, as it is conducted locally on the clients' devices. However, the global model is susceptible to data poisoning. We develop a privacy-preserving FL technique for a skin cancer dataset and show that the model is prone to data poisoning attacks. Ten clients train the model, but one of them intentionally introduces flipped labels as an attack. This reduces the accuracy of the global model. As the percentage of label flipping increases, there is a noticeable decrease in accuracy. We use a stochastic gradient descent optimization algorithm to find the most optimal accuracy for the model. Although FL can protect user privacy for healthcare diagnostics, it is also vulnerable to data poisoning, which must be addressed.