From Distributed Machine Learning To Federated Learning: In The View Of Data Privacy And Security

TL;DR

Federated learning enhances distributed machine learning by enabling decentralized data processing on devices, offering improved privacy and security through encryption and privacy-preserving techniques, while facing ongoing challenges and vulnerabilities.

Contribution

This survey comprehensively reviews the latest privacy and security mechanisms, attack models, and future challenges in federated learning.

Findings

Differential privacy and secure aggregation are key privacy techniques.

Federated learning reduces data breach risks by keeping data on devices.

Vulnerabilities include model inversion and poisoning attacks.

Abstract

Federated learning is an improved version of distributed machine learning that further offloads operations which would usually be performed by a central server. The server becomes more like an assistant coordinating clients to work together rather than micro-managing the workforce as in traditional DML. One of the greatest advantages of federated learning is the additional privacy and security guarantees it affords. Federated learning architecture relies on smart devices, such as smartphones and IoT sensors, that collect and process their own data, so sensitive information never has to leave the client device. Rather, clients train a sub-model locally and send an encrypted update to the central server for aggregation into the global model. These strong privacy guarantees make federated learning an attractive choice in a world where data breaches and information theft are common and serious threats. This survey outlines the landscape and latest developments in data privacy and security for federated learning. We identify the different mechanisms used to provide privacy and security, such as differential privacy, secure multi-party computation and secure aggregation. We also survey the current attack models, identifying the areas of vulnerability and the strategies adversaries use to penetrate federated systems. The survey concludes with a discussion on the open challenges and potential directions of future work in this increasingly popular learning paradigm.