ZKPoT Secures Federated Learning, Ensuring Privacy and Efficiency in Decentralized Systems ∞ Research

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Briefing

The paper addresses privacy and efficiency challenges inherent in blockchain-secured federated learning (FL) by proposing Zero-Knowledge Proof of Training (ZKPoT). This novel consensus mechanism leverages zero-knowledge succinct non-interactive arguments of knowledge (zk-SNARKs) to verify participants’ model performance without exposing sensitive underlying data, thereby enhancing security, scalability, and efficiency for decentralized FL architectures.

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Context

Prior to this research, blockchain-secured federated learning systems grappled with the limitations of conventional consensus mechanisms. Proof-of-Work (PoW) proved computationally expensive for resource-constrained environments, while Proof-of-Stake (PoS) introduced centralization risks by favoring participants with larger stakes. Learning-based consensus, while offering energy efficiency, inadvertently created privacy vulnerabilities through the exposure of model parameters during the necessary verification process, even when augmented with differential privacy techniques.

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Analysis

ZKPoT introduces a mechanism where clients generate zk-SNARK proofs to attest to their model’s accuracy on a public dataset without revealing the model parameters themselves. This fundamentally differs from previous approaches that either consumed excessive computational resources or compromised data privacy during model verification. The system integrates the InterPlanetary File System (IPFS) for efficient, decentralized storage of large files, such as global models and proofs, which significantly reduces on-chain communication overhead and storage costs. The proving and verification processes are further optimized through quantization techniques and by having a semi-honest task publisher assist in the setup phase.

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Parameters

Core Concept ∞ Zero-Knowledge Proof of Training (ZKPoT)
Cryptographic Primitive ∞ zk-SNARK (Zero-Knowledge Succinct Non-Interactive Argument of Knowledge)
Key Authors ∞ Tianxing Fu, Jia Hu, Geyong Min, Zi Wang
System Integration ∞ InterPlanetary File System (IPFS)
Consensus Mechanism ∞ Performance-based leader selection
Underlying zk-SNARK Scheme ∞ Groth16
Elliptic Curve ∞ BLS12-381
Programming Languages ∞ Python 3.10, Rust 1.77
Datasets for Evaluation ∞ CIFAR10, MNIST
Attack Resilience ∞ Privacy and Byzantine attacks

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Outlook

Future research in this area will likely focus on optimizing ZKPoT for broader application in diverse decentralized AI systems, exploring its integration with other privacy-enhancing technologies, and further reducing computational overhead for resource-constrained edge devices. This theory could unlock truly private and scalable collaborative AI development across various industries in the next 3-5 years, fostering new paradigms for data collaboration where privacy is foundational.

ZKPoT fundamentally redefines secure and private federated learning, establishing a robust framework for decentralized AI without compromising data confidentiality or system efficiency.

Signal Acquired from ∞ arxiv.org