ZK-HybridFL: Zero-Knowledge Proof-Enhanced Hybrid Ledger for Federated Learning

2026-01-29

AI Summary

This work addresses key challenges in decentralized federated learning, including scalability, secure verification, and defense against malicious nodes. The authors propose a novel framework that integrates a directed acyclic graph (DAG)-based main ledger with dedicated sidechains, uniquely combining zero-knowledge proofs (ZKPs) with a hybrid ledger architecture to enable efficient, privacy-preserving model update verification. By leveraging an event-driven smart contract system coordinated with oracles and incorporating a challenge protocol, the framework effectively identifies malicious participants and mitigates orphan attacks. Experimental results on image classification and language modeling tasks demonstrate significant improvements in convergence speed, accuracy, and robustness, achieving sub-second on-chain verification while reducing communication latency and perplexity.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Natural Language Processing: Safety and Robustness
• Search and Optimization: Distributed Search

Application Category

• Security and Privacy: Blockchains and distributed ledgers
• Systems and Infrastructure for Web, Mobile and WoT: Decentralized Web and Fediverse systems
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

Federated learning (FL) enables collaborative model training while preserving data privacy, yet both centralized and decentralized approaches face challenges in scalability, security, and update validation. We propose ZK-HybridFL, a secure decentralized FL framework that integrates a directed acyclic graph (DAG) ledger with dedicated sidechains and zero-knowledge proofs (ZKPs) for privacy-preserving model validation. The framework uses event-driven smart contracts and an oracle-assisted sidechain to verify local model updates without exposing sensitive data. A built-in challenge mechanism efficiently detects adversarial behavior. In experiments on image classification and language modeling tasks, ZK-HybridFL achieves faster convergence, higher accuracy, lower perplexity, and reduced latency compared to Blade-FL and ChainFL. It remains robust against substantial fractions of adversarial and idle nodes, supports sub-second on-chain verification with efficient gas usage, and prevents invalid updates and orphanage-style attacks. This makes ZK-HybridFL a scalable and secure solution for decentralized FL across diverse environments.

Problem

• Federated Learning
• Scalability
• Security
• Update Validation
• Decentralized Framework

Innovation

• Zero-Knowledge Proofs
• Directed Acyclic Graph
• Federated Learning
• Sidechain
• Smart Contracts