Decentralized Verification of Federated Learning Models

Abstract

Federated Learning (FL) has emerged as a promising paradigm for training machine learning models on decentralized data sources without direct data sharing. However, ensuring the integrity and accuracy of these models remains a significant challenge. Traditional verification methods often rely on centralized aggregation, inherently compromising user privacy and introducing a single point of failure. This paper proposes a novel blockchain-based system for decentralized verification of FL models. The system leverages cryptographic proofs, specifically zero-knowledge Succinct Non-Interactive Argument of Knowledge (zk-SNARKs), allowing nodes to independently verify model updates without revealing the underlying data. This approach eliminates centralized aggregation, enhancing privacy and establishing a verifiable, distributed ledger of model updates. The core claim is that current decentralized verification methods are computationally expensive and rely on centralized aggregation, thus, this system offers a truly decentralized and privacy-preserving verification framework.