Privacy Threats Analysis to Secure Federated Learning

Understand

Federated learning is emerging as a machine learning technique that trains a model across multiple decentralized parties.

• It is renowned for preserving privacy as the data never leaves the computational devices, and recent approaches further enhance its privacy by hiding messages transferred in encryption.
• However, we found that despite the efforts, federated learning remains privacy-threatening, due to its interactive nature across different parties.
• In this paper, we analyze the privacy threats in industrial-level federated learning frameworks with secure computation, and reveal such threats widely exist in typical machine learning models such as linear regression, logistic regression and decision tree.