Zero-Knowledge Quantized Weighted Majority Algorithm

Abstract

The rise of collaborative AI, particularly in distributed Mixture-of-Experts (MoE) systems, has created a critical challenge: how to ensure trust and transparency when aggregating proprietary models from different providers. To address this, we introduce a novel cryptographic protocol ZQ-WMA that enables verifiable and privacy-preserving online learning. Our method integrates zero-knowledge proofs with a quantized version of the Weighted Majority Algorithm, allowing a central aggregator to publicly prove it is honestly combining expert advice and updating weights according to the agreed-upon rules, all without revealing any confidential model parameters.This approach ensures that expert contributions are evaluated fairly and protects valuable intellectual property. Our analysis reveals that the quantization necessary for the zero-knowledge proofs can counter-intuitively enhance prediction accuracy, a phenomenon we attribute to the maximal entropy random walks. Furthermore, our benchmarks demonstrate the efficiency of this method, showing proof generation complexity less than 10% of a standard SHA256 hash function, with O(1) proof size and verification time. This work provides a practical and scalable framework for building trustworthy collaborative AI systems.