# Advancing Blockchain-based Federated Learning through Verifiable Off-chain Computations

arXiv: https://arxiv.org/abs/2206.11641 · HTML: https://mlsanity.com/paper/2206.11641/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jun 2022.

## Built on by (most cited first, 7 of 7)

- [zkDFL: An efficient and privacy-preserving decentralized federated learning with zero-knowledge proof](https://mlsanity.com/paper/2312.04579/) (2023)
- [TrustChain: A Blockchain Framework for Auditing and Verifying Aggregators in Decentralized Federated Learning](https://mlsanity.com/paper/2502.16406/) (2025)
- [Zero-Knowledge Federated Learning: A New Trustworthy and Privacy-Preserving Distributed Learning Paradigm](https://mlsanity.com/paper/2503.15550/) (2025)
- [A Blockchain-based Platform for Reliable Inference and Training of Large-Scale Models](https://mlsanity.com/paper/2305.04062/) (2023)
- [End-to-End Verifiable Decentralized Federated Learning](https://mlsanity.com/paper/2404.12623/) (2024)
- [SoK: Verifiable Cross-Silo FL](https://mlsanity.com/paper/2410.09124/) (2024)
- [FedStrategist: A Meta-Learning Framework for Adaptive and Robust Aggregation in Federated Learning](https://mlsanity.com/paper/2507.14322/) (2025)

## Abstract (arXiv)

Federated learning may be subject to both global aggregation attacks and distributed poisoning attacks. Blockchain technology along with incentive and penalty mechanisms have been suggested to counter these. In this paper, we explore verifiable off-chain computations using zero-knowledge proofs as an alternative to incentive and penalty mechanisms in blockchain-based federated learning. In our solution, learning nodes, in addition to their computational duties, act as off-chain provers submitting proofs to attest computational correctness of parameters that can be verified on the blockchain. We demonstrate and evaluate our solution through a health monitoring use case and proof-of-concept implementation leveraging the ZoKrates language and tools for smart contract-based on-chain model management. Our research introduces verifiability of correctness of learning processes, thus advancing blockchain-based federated learning.