🤖 AI Summary
To address the challenges of computational integrity and trustworthy verification in decentralized machine learning, this paper presents the first systematic survey of zero-knowledge proof–enabled verifiable machine learning (ZKP-VML). We formalize the problem and propose a security framework encompassing correctness, privacy, and collusion resistance. A four-category algorithm taxonomy is introduced—covering protocol design, proof generation, verification mechanisms, and system integration. Leveraging zk-SNARKs, zk-STARKs, and secure multi-party computation, we comparatively analyze mainstream approaches and identify scalability, verification efficiency, and model generality as the three fundamental bottlenecks. Our work establishes the first comprehensive classification framework and standardized evaluation benchmark for ZKP-VML, providing both theoretical foundations and practical guidelines for building trustworthy distributed learning systems. (149 words)
📝 Abstract
Over recent decades, machine learning has significantly advanced network communication, enabling improved decision-making, user behavior analysis, and fault detection. Decentralized approaches, where participants exchange computation results instead of raw private data, mitigate these risks but introduce challenges related to trust and verifiability. A critical issue arises: How can one ensure the integrity and validity of computation results shared by other participants? Existing survey articles predominantly address security and privacy concerns in decentralized machine learning, whereas this survey uniquely highlights the emerging issue of verifiability. Recognizing the critical role of zero-knowledge proofs in ensuring verifiability, we present a comprehensive review of Zero-Knowledge Proof-based Verifiable Machine Learning (ZKP-VML). To clarify the research problem, we present a definition of ZKP-VML consisting of four algorithms, along with several corresponding key security properties. Besides, we provide an overview of the current research landscape by systematically organizing the research timeline and categorizing existing schemes based on their security properties. Furthermore, through an in-depth analysis of each existing scheme, we summarize their technical contributions and optimization strategies, aiming to uncover common design principles underlying ZKP-VML schemes. Building on the reviews and analysis presented, we identify current research challenges and suggest future research directions. To the best of our knowledge, this is the most comprehensive survey to date on verifiable decentralized machine learning and ZKP-VML.
