Synergistic Disruption: Harnessing AI and Blockchain for Enhanced Privacy and Security in Federated Learning

Sandi Rahmadika; Winda Agustiarmi; Ryan Fikri; Bruno Joachim Kweka

doi:10.15575/join.v10i1.1392

Authors

Sandi Rahmadika Department of Electronic Engineering, Faculty of Engineering Faculty of Engineering, Universitas Negeri Padang, Sumatera Barat, Indonesia https://orcid.org/0000-0002-7848-6579
Winda Agustiarmi Department of Electronic Engineering, Faculty of Engineering Faculty of Engineering, Universitas Negeri Padang, Sumatera Barat, Indonesia
Ryan Fikri Department of Electronic Engineering, Faculty of Engineering Faculty of Engineering, Universitas Negeri Padang, Sumatera Barat, Indonesia
Bruno Joachim Kweka Cyber Studies, The University of Tulsa, United States of America, 800 S Tucker Dr, Tulsa, OK 74104, United States

DOI:

https://doi.org/10.15575/join.v10i1.1392

Keywords:

Artificial Intelligence, Blockchain, Federated Learning, Privacy, Smart Contracts

Abstract

Combining blockchain technology with artificial intelligence (AI) offers revolutionary possibilities for developing strong solutions that capitalize on each technology's own advantages. Blockchain technology makes self-executing agreements possible by enabling smart contracts, which reduce the need for middlemen and increase efficiency by precisely encoding contractual terms in code. By using AI oracles, these contracts can communicate with outside data sources and make well-informed decisions based on actual occurrences. Additionally, there is a lot of potential for improving machine learning and data interchange in terms of privacy, security, and transparency through the integration of blockchain with federated learning. In order to provide accountability and transparency, the blockchain's immutable ledger can painstakingly record every transaction that takes place during the federated learning process, from data submissions to model modifications and remuneration. Participants in federated learning networks also develop trust because of blockchain's transparency and resistance to tampering. Strong participant verification procedures are put in place to strengthen data integrity and model updates, which raises the system's overall reliability. In the end, this chapter examines novel research avenues for combining blockchain technology with federated learning, providing practical methods and strategies to improve transaction security and privacy and opening the door to a new era of reliable and effective machine learning applications.

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