Role-based federated learning exploiting IPFS for privacy enhancement in IoT environment

As the IoT expands exponentially, the amount of data generated by individuals has increased. To process big data efficiently, machine learning (especially deep learning) has emerged. However, existing machine learning has the disadvantage of being vulnerable to data privacy because it sends raw data to the center. Therefore, federated learning (FL) was introduced to address this privacy problem, in which only learning parameters are sent to the center after training the user's own local model with their own raw data. However, FL remains vulnerable to various attacks. In this paper, we propose an efficient and safe FL framework using the Interplanetary File System (IPFS) that minimizes the effect of data poisoning attacks on FL. In this system, the roles of nodes are divided into three: leader node, A-node (Aggregation-node), and T-node (Training-node). In this way, the A-node and T-node cannot manipulate the learning information, allowing the sharing of information and data safely through IPFS while protecting raw data with a similarity-based data shuffling scheme used by the A-node. Moreover, nodes with high accuracy receive more incentives and learning motivation, enhancing the overall efficiency of the network. Finally, the efficiency of the system is verified through related simulations.