# End-to-End Verifiable Decentralized Federated Learning

arXiv: https://arxiv.org/abs/2404.12623 · HTML: https://mlsanity.com/paper/2404.12623/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 43 references that are in the map.
- First posted on arXiv 19 Apr 2024.

## Builds on (most cited first, 12 of 12)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Federated Learning for Internet of Things: A Comprehensive Survey](https://mlsanity.com/paper/2104.07914/) (2021)
- [Fairness, Integrity, and Privacy in a Scalable Blockchain-based Federated Learning System](https://mlsanity.com/paper/2111.06290/) (2021)
- [Hierarchical Federated Learning based Anomaly Detection using Digital Twins for Smart Healthcare](https://mlsanity.com/paper/2111.12241/) (2021)
- [Towards Verifiable Federated Learning](https://mlsanity.com/paper/2202.08310/) (2022)
- [Zero-Knowledge Proof-based Practical Federated Learning on Blockchain](https://mlsanity.com/paper/2304.05590/) (2023)
- [Advancing Blockchain-based Federated Learning through Verifiable Off-chain Computations](https://mlsanity.com/paper/2206.11641/) (2022)
- [Towards Fair and Privacy-Preserving Federated Deep Models](https://mlsanity.com/paper/1906.01167/) (2019)
- [Blockchain-based Trustworthy Federated Learning Architecture](https://mlsanity.com/paper/2108.06912/) (2021)
- [Non-Disclosing Credential On-chaining for Blockchain-based Decentralized Applications](https://mlsanity.com/paper/2209.09584/) (2022)
- [Trustworthy Pre-Processing of Sensor Data in Data On-chaining Workflows for Blockchain-based IoT Applications](https://mlsanity.com/paper/2110.15869/) (2021)

## Abstract (arXiv)

Verifiable decentralized federated learning (FL) systems combining blockchains and zero-knowledge proofs (ZKP) make the computational integrity of local learning and global aggregation verifiable across workers. However, they are not end-to-end: data can still be corrupted prior to the learning. In this paper, we propose a verifiable decentralized FL system for end-to-end integrity and authenticity of data and computation extending verifiability to the data source. Addressing an inherent conflict of confidentiality and transparency, we introduce a two-step proving and verification (2PV) method that we apply to central system procedures: a registration workflow that enables non-disclosing verification of device certificates and a learning workflow that extends existing blockchain and ZKP-based FL systems through non-disclosing data authenticity proofs. Our evaluation on a prototypical implementation demonstrates the technical feasibility with only marginal overheads to state-of-the-art solutions.