用零知识证明提升去中心化联邦学习效率，降低链上开销。
AutoDFL: A Scalable and Automated Reputation-Aware Decentralized Federated Learning
- 采用zk-Rollups实现链下计算，提升系统吞吐量。
- 在多种负载下平均吞吐达3000 TPS，Gas消耗降低20倍。
- 自动公平的信誉机制激励参与者，适合高安全需求场景。
区块链联邦学习（BFL）结合联邦学习与区块链技术，提升了协同机器学习中的隐私、安全和透明性。然而，实现BFL框架在可扩展性和成本效益方面面临挑战。带有信誉感知的BFL更是困难，因为区块链验证者需处理联邦学习交易及评估任务和聚合信誉的交易，导致链上拥堵加剧，性能下降。为在保持底层区块链安全性的同时提升效率、增强可扩展性并降低链上信誉管理成本，本文提出AutoDFL——一种可扩展且自动化的信誉感知去中心化联邦学习框架。AutoDFL利用zk-Rollups作为第二层扩展方案，显著提升性能。此外，引入自动化且公平的信誉模型以激励联邦学习参与者。我们构建了原型系统进行准确评估。在多种自定义工作负载下测试，AutoDFL平均吞吐量超过3000 TPS，Gas消耗最多降低20倍。
原文摘要 · Abstract (English)
Blockchained federated learning (BFL) combines the concepts of federated learning and blockchain technology to enhance privacy, security, and transparency in collaborative machine learning models. However, implementing BFL frameworks poses challenges in terms of scalability and cost-effectiveness. Reputation-aware BFL poses even more challenges, as blockchain validators are tasked with processing federated learning transactions along with the transactions that evaluate FL tasks and aggregate reputations. This leads to faster blockchain congestion and performance degradation. To improve BFL efficiency while increasing scalability and reducing on-chain reputation management costs, this paper proposes AutoDFL, a scalable and automated reputation-aware decentralized federated learning framework. AutoDFL leverages zk-Rollups as a Layer-2 scaling solution to boost the performance while maintaining the same level of security as the underlying Layer-1 blockchain. Moreover, AutoDFL introduces an automated and fair reputation model designed to incentivize federated learning actors. We develop a proof of concept for our framework for an accurate evaluation. Tested with various custom workloads, AutoDFL reaches an average throughput of over 3000 TPS with a gas reduction of up to 20X.
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