用区块链解决联邦学习的激励与信任问题，让参与更公平透明。
Blockchain-based Framework for Scalable and Incentivized Federated Learning
- 通过智能合约自动管理注册、验证与奖励分配
- 混合激励机制确保贡献公平且长期参与
- 适合大规模联邦学习场景，尤其资源密集型模型
联邦学习（FL）可在不共享原始数据的前提下实现协同建模，保护隐私并利用分布式数据集。然而传统FL系统依赖中心化聚合机制，存在信任风险、单点故障及对客户端有效贡献激励不足等问题。随着训练资源密集型模型（如大语言模型）规模扩大，这些问题愈发突出。本文提出一种基于区块链的联邦学习框架，结合智能合约与新型混合激励机制，自动化完成客户端注册、更新验证、奖励分发及全局状态维护。该混合激励机制融合链上对齐奖励、链下公平性检查和一致性乘数，保障公平性、透明度与持续参与。通过气体成本分析评估框架在不同规模下的可行性。
原文摘要 · Abstract (English)
Federated Learning (FL) enables collaborative model training without sharing raw data, preserving privacy while harnessing distributed datasets. However, traditional FL systems often rely on centralized aggregating mechanisms, introducing trust issues, single points of failure, and limited mechanisms for incentivizing meaningful client contributions. These challenges are exacerbated as FL scales to train resource-intensive models, such as large language models (LLMs), requiring scalable, decentralized solutions. This paper presents a blockchain-based FL framework that addresses these limitations by integrating smart contracts and a novel hybrid incentive mechanism. The framework automates critical FL tasks, including client registration, update validation, reward distribution, and maintaining a transparent global state. The hybrid incentive mechanism combines on-chain alignment-based rewards, off-chain fairness checks, and consistency multipliers to ensure fairness, transparency, and sustained engagement. We evaluate the framework through gas cost analysis, demonstrating its feasibility for different scales of federated learning scenarios.
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