Secure mobile cloud data using federated learning and blockchain technology

Abstract

In the current era, mobile cloud (MC) transactions raise concerns over the data stored in the MC. These data can be tampered with by third parties, leading to data loss and information misplacement. Such security breaches can be mitigated by implementing federated learning (FL). FL refers to a distributed data learning approach that trains data without revealing the information to the server or coordinator. It uses the current model data for training and then sends the updated model to the coordinator or server. The server collects the updated trained models from all clients and aggregates them into a single global model. This updated model is then communicated back to the clients. FL, when implemented with MC, protects user privacy, ensures efficient learning, and achieves higher accuracy compared to traditional machine learning algorithms. We propose the implementation of MC FL using blockchain, a model designed to protect user data by maintaining it on edge devices and sending the updated model to the server after training. Finally, the data-generated model will be stored in the blockchain network, preventing data tampering and providing a higher level of security and privacy for the data.