ESTABLISHING DIGITAL TRUST: A REVIEW OF PROTOCOLS FOR MACHINE LEARNING ALGORITHM CONSENSUS IN CYBERSPACE

Abstract

The increase in the number of machine-learning models in cyberspace, from collaborative federated-learning systems to autonomous multi-agent systems, has created a desperate need for mechanisms that enable such algorithms to trust one another. Traditional security paradigms focus on protecting systems against external adversaries, whereas the algorithmic trust paradigm aims to provide guarantees of reliability, integrity, and good intent within models themselves. The article is a thorough review of the still-developing protocols and mechanisms that machine-learning algorithms use to build, maintain, and validate trust among peers. The protocols are divided into four main categories; (1) Byzantine-Resistant Federated Learning, which provides thin slices of malicious behaviour in collaborative settings by robustly statistical aggregations; (2) Trusted Execution Environments (TEEs), which provide hardware-level assurances against malicious code and executed models; (3) Blockchain and Decentralized Consensus, which provides immutable audit logs and decentralized reputation management by distributing ledgers and using smart contracts; and (4) Game-Theoretic and Reputation-Based Models, which encore In each category to discuss the principles, basic algorithms as well as real-life applications. The existing literature is critical for assessing the merits and demerits of each methodological approach relative to others. It then outlines future research directions, including the use of adaptive adversarial systems, the introduction of privacy-preserving techniques to establish trust, and the adoption of standardized systems; all of which are expected to help achieve a verified, trust-based system of interacting machine intelligences.