A Study of Federated Learning with Internet of Things for Data Privacy and Security using Privacy Preserving Techniques

Abstract

Privacy leakage that occurs when many IoT devices are utilized for training centralized models, a new distributed learning framework known as federated learning was created, where devices train models together while keeping their private datasets local. In a federated learning setup, a central aggregator coordinates the efforts of several clients working together to solve machine learning issues. The privacy of each device's data is protected by this setup's decentralized training data. Federated learning reduces traditional centralized machine learning systems' systemic privacy issues and costs by emphasizing local processing and model transfer. Client information is stored locally and cannot be copied or shared. By utilizing a centralized server, federated learning enables each participant's device to collect data locally for training purposes before sending the resulting model to the server for aggregate and subsequent distribution. As a means of providing a comprehensive review and encouraging further research into the topic, we introduce the works of federated learning from five different vantage points: data partitioning, privacy method, machine learning model, communication architecture, and systems heterogeneity. Then, we organize the issues plaguing federated learning today and the potential avenues for a prospective study. Finally, we provide a brief overview of the features of existing federated knowledge and discuss how it is currently being used in the field.