解决开放联邦学习中奖励结算的高成本难题，实现去中心化且可扩展的激励机制。
SettleFL: Trustless and Scalable Reward Settlement Protocol for Federated Learning on Permissionless Blockchains (Extended version)
- 采用乐观执行与争议仲裁结合的承诺-挑战机制，降低链上开销。
- 支持每轮训练即时终局性验证，保障结果不可篡改。
- 适用于大规模参与者场景，实测800人时仍具低成本优势。
在无中心权威的开放联邦学习环境中，协作公平性依赖于去中心化的奖励结算，但许可链的高昂成本与模型训练高频迭代特性直接冲突。现有方案或牺牲去中心化，或因链上成本线性增长而面临可扩展性瓶颈。为此，我们提出SettleFL，一种无需信任且可扩展的奖励结算协议，通过一套可互操作的双策略设计，最小化整体经济摩擦。基于共享的领域专用电路架构，SettleFL提供两种协同策略：(1) 承诺-挑战变体，利用乐观执行与争议驱动仲裁降低链上开销；(2) 承诺-带证明变体，通过每轮有效性证明实现即时终局性。该设计可灵活适应不同延迟与成本约束，且无需可信协调即可保证理性鲁棒性。我们结合真实联邦学习负载与受控仿真进行了广泛实验，结果表明，当参与者规模扩展至800人时，SettleFL仍保持实用性，并显著降低Gas费用。
原文摘要 · Abstract (English)
In open Federated Learning (FL) environments where no central authority exists, ensuring collaboration fairness relies on decentralized reward settlement, yet the prohibitive cost of permissionless blockchains directly clashes with the high-frequency, iterative nature of model training. Existing solutions either compromise decentralization or suffer from scalability bottlenecks due to linear on-chain costs. To address this, we present SettleFL, a trustless and scalable reward settlement protocol designed to minimize total economic friction by offering a family of two interoperable protocols. Leveraging a shared domain-specific circuit architecture, SettleFL offers two interoperable strategies: (1) a Commit-and-Challenge variant that minimizes on-chain costs via optimistic execution and dispute-driven arbitration, and (2) a Commit-with-Proof variant that guarantees instant finality through per-round validity proofs. This design allows the protocol to flexibly adapt to varying latency and cost constraints while enforcing rational robustness without trusted coordination. We conduct extensive experiments combining real FL workloads and controlled simulations. Results show that SettleFL remains practical when scaling to 800 participants, achieving substantially lower gas cost.
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