Privacy Enhanced Federated Learning in Encrypted Anonymous Personal Device Domain

The increase in privacy concerns and the introduction of privacy and data protection legislation compel organisations to reevaluate their practices regarding traditional machine learning. The aggregation and management of users’ private data on the central server may contravene regulations if not properly administered. Federated learning provides a technique that eliminates the necessity of uploading users’ data to the server. It facilitates substantial learning by collaboratively training on each client’s devices and pooling the model gradient changes. Federated learning, augmented with a proxy as an intermediary and encrypted model parameters, will enhance anonymity, privacy, and data protection against malicious threats, including membership inference adversaries. Nonetheless, encrypted data incurs costs for customers’ communication and data size that exceed twice the original size. Our paper seeks to resolve these issues. We present two secure approaches for effective communication in an anonymous encrypted federated learning framework as our contribution. Additionally, our experiments demonstrated that it is feasible to attain equivalent communication costs as in non-encrypted scenarios. We provide recommendations in the conclusion for the effective implementation of privacy-preserving federated learning in the area of personal devices.