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

TL;DR

SettleFL introduces a scalable, trustless reward settlement protocol for federated learning on permissionless blockchains, reducing costs and ensuring fairness without central authority or trusted parties.

Contribution

It proposes a novel family of protocols combining optimistic and proof-based strategies to improve scalability and decentralization in blockchain-based federated learning reward systems.

Findings

Achieves lower gas costs at scale up to 800 participants.

Provides flexible strategies for different latency and cost requirements.

Ensures rational robustness without trusted coordination.

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.