SettleFL: Trustless and Scalable Reward Settlement Protocol for Federated Learning on Permissionless Blockchains (Extended version)

2026-02-26

AI Summary

This work addresses the scalability challenge in open federated learning, where frequent training rounds conflict with the high on-chain costs of public blockchains. To reconcile this tension, the authors propose a trustless, dual-mode reward settlement protocol that integrates domain-specific circuits with two interoperable strategies: a Commit-and-Challenge mechanism based on optimistic execution and dispute arbitration, and a Commit-with-Proof mechanism leveraging validity proofs. Operating under full decentralization, the design substantially reduces gas consumption while remaining practical even at a scale of 800 participants. The resulting system achieves low-friction, incentive-compatible, and coordination-free settlement without relying on any trusted third party.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Mechanism Design

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Decentralized Web and Fediverse systems
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

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 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.

Problem

• Federated Learning
• Reward Settlement
• Permissionless Blockchains
• Scalability
• Decentralization

Innovation

• Federated Learning
• Permissionless Blockchain
• Reward Settlement
• Zero-Knowledge Proofs
• Scalability