用零知识证明增强去中心化联邦学习，让模型更新既安全又高效。
ZK-HybridFL: Zero-Knowledge Proof-Enhanced Hybrid Ledger for Federated Learning
- 结合DAG账本与侧链，用零知识证明验证模型更新
- 实验显示收敛更快、准确率更高、延迟更低
- 适合需要高安全性和抗攻击能力的分布式学习场景
联邦学习（FL）可在保护数据隐私的同时实现协同建模，但集中式和去中心化方案均面临可扩展性、安全性和更新验证难题。本文提出ZK-HybridFL，一种融合有向无环图（DAG）账本、专用侧链与零知识证明（ZKPs）的去中心化安全框架，用于隐私保护的模型验证。通过事件驱动的智能合约与预言机辅助侧链，实现本地模型更新的验证而无需暴露敏感数据。内置挑战机制可有效检测恶意行为。在图像分类与语言建模任务上的实验表明，相较于Blade-FL与ChainFL，ZK-HybridFL具备更快的收敛速度、更高的准确率、更低的困惑度以及更短的延迟。系统对大量恶意节点和空闲节点仍保持鲁棒性，支持亚秒级链上验证且气体开销低，可防范无效更新与孤儿攻击。该框架适用于多样环境下的可扩展、安全的去中心化联邦学习。
原文摘要 · Abstract (English)
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.
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