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.

翻译：在缺乏中心化权威的开放联邦学习环境中，确保协作公平性依赖于去中心化的奖励结算，然而无许可区块链的高昂成本与模型训练的高频迭代特性直接冲突。现有解决方案要么牺牲去中心化特性，要么因链上成本线性增长而面临可扩展性瓶颈。为解决此问题，我们提出SettleFL——一种无需信任且可扩展的奖励结算协议，通过提供两种可互操作的协议族来最小化总体经济摩擦。基于共享的领域专用电路架构，SettleFL提供两种互操作策略：（1）采用提交-挑战模式的变体，通过乐观执行与争议驱动仲裁实现链上成本最小化；（2）采用提交-证明模式的变体，通过每轮有效性证明保障即时最终性。该设计使协议能灵活适应不同的延迟与成本约束，同时在无需可信协调的情况下实现理性鲁棒性。我们结合真实联邦学习工作负载与受控仿真开展了大量实验。结果表明，在扩展至800名参与者时，SettleFL仍保持实用性，并显著降低了燃气成本。