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[2602.23167] SettleFL: Trustless and Scalable Reward Settlement Protocol for Federated Learning on Permissionless Blockchains (Extended version)
Summary
SettleFL introduces a scalable and trustless reward settlement protocol for federated learning on permissionless blockchains, addressing cost and efficiency challenges.
Why It Matters
As federated learning grows, ensuring fair collaboration without a central authority is crucial. SettleFL offers a solution that minimizes costs while maintaining decentralization, making it significant for developers and researchers in blockchain and machine learning fields.
Key Takeaways
- SettleFL provides two interoperable protocols to reduce economic friction in federated learning.
- The Commit-and-Challenge variant minimizes on-chain costs through optimistic execution.
- The Commit-with-Proof variant ensures instant finality with validity proofs.
- SettleFL scales effectively to 800 participants while significantly lowering gas costs.
- The protocol adapts to varying latency and cost constraints, enhancing its practical application.
Computer Science > Cryptography and Security arXiv:2602.23167 (cs) [Submitted on 26 Feb 2026] Title:SettleFL: Trustless and Scalable Reward Settlement Protocol for Federated Learning on Permissionless Blockchains (Extended version) Authors:Shuang Liang (1), Yang Hua (2), Linshan Jiang (3), Peishen Yan (1), Tao Song (1), Bin Yao (1), Haibing Guan (1) ((1) Shanghai Jiao Tong University, (2) Queen's University Belfast, (3) National University of Singapore) View a PDF of the paper titled SettleFL: Trustless and Scalable Reward Settlement Protocol for Federated Learning on Permissionless Blockchains (Extended version), by Shuang Liang (1) and 8 other authors View PDF HTML (experimental) Abstract: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 ...
