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