Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices

Home appliance manufacturers strive to obtain feedback from users to improve their products and services to build a smart home system. To help manufacturers develop a smart home system, we design a federated learning (FL) system leveraging the reputation mechanism to assist home appliance manufacturers to train a machine learning model based on customers' data. Then, manufacturers can predict customers' requirements and consumption behaviors in the future. The working flow of the system includes two stages: in the first stage, customers train the initial model provided by the manufacturer using both the mobile phone and the mobile edge computing (MEC) server. Customers collect data from various home appliances using phones, and then they download and train the initial model with their local data. After deriving local models, customers sign on their models and send them to the blockchain. In case customers or manufacturers are malicious, we use the blockchain to replace the centralized aggregator in the traditional FL system. Since records on the blockchain are untampered, malicious customers or manufacturers' activities are traceable. In the second stage, manufacturers select customers or organizations as miners for calculating the averaged model using received models from customers. By the end of the crowdsourcing task, one of the miners, who is selected as the temporary leader, uploads the model to the blockchain. To protect customers' privacy and improve the test accuracy, we enforce differential privacy on the extracted features and propose a new normalization technique. We experimentally demonstrate that our normalization technique outperforms batch normalization when features are under differential privacy protection. In addition, to attract more customers to participate in the crowdsourcing FL task, we design an incentive mechanism to award participants.Comment: This paper appears in IEEE Internet of Things Journal (IoT-J

New Advances in Kidney Transplantation

This Special Issue in renal transplantation covers a variety of clinical and research areas in kidney transplantation. The recent decade is associated with an ongoing shortage of organs for transplantation with efforts to increase the organ pool with DCDs and extended criteria donors. However, with the increasing success rate of kidney transplants, there is also a growth in the candidate list because of removal of the age barrier and transplantation of high risk patients with other comorbidities. The future seems promising with the development of innovative non-invasive technologies introducing biomarkers for diagnosis of rejection and ischemic reperfusion injury, use of cell therapy for tolerance induction, development of artificial organs, and overcoming immune and non-immune barriers in xenotransplantation. This Special Issue will touch some of these topics that are in the frontiers of the modern era of kidney transplantation

Progress Report No. 14

Progress report of the Biomedical Computer Laboratory, covering period 1 July 1977 to 30 June 1978