Histopathological Image Classification and Vulnerability Analysis using
  Federated Learning
Sankalp Vyas Amar Nath Patra Raj Mani Shukla
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.