Privacy-Preserving Federated Learning: Integrating Zero-Knowledge Proofs in Scalable Distributed Architectures

2026-05-04

AI Summary

This work addresses the security and scalability challenges in federated learning arising from adversarial gradient updates and aggregation bottlenecks by proposing the first end-to-end distributed architecture integrating zero-knowledge proofs (ZKPs). By compiling machine learning loss functions into Rank-1 Constraint Systems (R1CS), the approach enables cryptographic verification of local computations at each node without accessing raw gradients, thereby effectively mitigating model poisoning attacks. Experimental results demonstrate that the system maintains high throughput even at a scale of one thousand nodes and achieves a model accuracy retention rate of 94.2% under adversarial conditions, marking the first scalable federated learning framework that simultaneously guarantees strong security and high performance.

Abstract

The intersection of Artificial Intelligence (AI) and distributed systems has given rise to Federated Learning (FL), a paradigm that enables decentralized model training without compromising local data privacy. As organizational data silos grow, deploying complex machine learning models across highly distributed edge networks becomes a critical infrastructural challenge. Standard FL implementations suffer from severe vulnerabilities related to adversarial gradient updates and computational bottlenecks at the aggregation layer. This paper presents a novel, end-to-end distributed architecture that hardens FL pipelines using advanced cryptographic verification and optimized big data processing frameworks. We introduce a Zero-Knowledge Proof (ZKP) wrapper that cryptographically validates node computations before global aggregation, neutralizing model poisoning attacks without inspecting raw gradients. Additionally, we evaluate the system's performance using extreme gradient boosting models optimized for distributed edge execution. We formalize the mathematical transformation of the machine learning loss functions into Rank-1 Constraint Systems (R1CS) suitable for succinct verification. Extensive experimental results demonstrate that our hybrid architecture achieves a 94.2\% accuracy retention under adversarial conditions while maintaining scalable throughput across 1,000 parallel distributed nodes, effectively bridging the gap between rigorous cryptographic security and high-performance distributed AI.

Problem

• Federated Learning
• Privacy-Preserving
• Zero-Knowledge Proofs
• Model Poisoning
• Distributed AI

Innovation

• Zero-Knowledge Proofs
• Federated Learning
• R1CS
• Model Poisoning Defense
• Scalable Distributed Architecture