Federated Learning with Blockchain-Enhanced Machine Unlearning: A Trustworthy Approach

TL;DR

This paper proposes a blockchain-based framework for trustworthy machine unlearning in federated learning, enhancing security, transparency, and efficiency in IoT environments to meet privacy and data deletion requirements.

Contribution

It introduces a novel blockchain-integrated federated learning framework that provides verifiable certification of unlearning, improving trustworthiness and security in IoT settings.

Findings

Blockchain ensures immutable records of unlearning requests.

The framework enhances data security and privacy.

System responsiveness is maintained in IoT scenarios.

Abstract

With the growing need to comply with privacy regulations and respond to user data deletion requests, integrating machine unlearning into IoT-based federated learning has become imperative. Traditional unlearning methods, however, often lack verifiable mechanisms, leading to challenges in establishing trust. This paper delves into the innovative integration of blockchain technology with federated learning to surmount these obstacles. Blockchain fortifies the unlearning process through its inherent qualities of immutability, transparency, and robust security. It facilitates verifiable certification, harmonizes security with privacy, and sustains system efficiency. We introduce a framework that melds blockchain with federated learning, thereby ensuring an immutable record of unlearning requests and actions. This strategy not only bolsters the trustworthiness and integrity of the federated learning model but also adeptly addresses efficiency and security challenges typical in IoT environments. Our key contributions encompass a certification mechanism for the unlearning process, the enhancement of data security and privacy, and the optimization of data management to ensure system responsiveness in IoT scenarios.