Privacy Preservation in Cloud-Based Distributed Learning through Data Encoding and Partitioning

This paper explores privacy-preserving training methods for machine learning models, crucial for protecting sensitive data during cloud-based model training. We propose a novel approach utilizing image encoding and partitioning to train models on remote servers. By partitioning images into encoded patches distributed across servers, each training an independent model, we ensure privacy and resilience against attacks. Our findings demonstrate the feasibility of training private datasets on cloud platforms with minimal accuracy loss, offering a high level of privacy at low cost. A critical aspect of our approach lies in its ability to uphold privacy without any modifications to the training software on cloud servers, unlike methods such as homomorphic encryption, which demand the utilization of specialized software.