Analysis of privacy preservation enhancements in federated learning frameworks

Abstract

Machine learning (ML) plays a growing role in the Internet of Things (IoT) applications and has efficiently contributed to many aspects, both for businesses and consumers, including proactive intervention, tailored experiences, and intelligent automation. Traditional cloud computing machine learning applications need the data, generated by IoT devices, to be uploaded and processed on a central server giving data access to third parties raising privacy and data ownership concerns. Federated learning (FL) is able to overcome these privacy concerns by enabling an on-device collaborative training of a machine learning model without sharing any data over the network. However, model sharing can also potentially reveal sensitive information. Therefore, federated learning needs additional privacy-preserving techniques to enable fully private machine learning model sharing and training. In this chapter, privacy-preserving techniques for federated learning are studied. In addition, a comparative analysis of state-of-the-art federated learning frameworks against privacy-preserving techniques is presented. The analysis comprises the identification of main advantages and disadvantages for eight FL frameworks as well as the investigation of the frameworks under criteria related to their FL features and privacy preservation options.