# Towards Verifiable Federated Learning

arXiv: https://arxiv.org/abs/2202.08310 · HTML: https://mlsanity.com/paper/2202.08310/

## Key facts

- Cited by 11 papers in the map (data through 7 Oct 2026).
- Builds on 14 of its 59 references that are in the map.
- First posted on arXiv 15 Feb 2022.

## Builds on (most cited first, 14 of 14)

- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Federated Optimization: Distributed Machine Learning for On-Device Intelligence](https://mlsanity.com/paper/1610.02527/) (2016)
- [A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI](https://mlsanity.com/paper/1907.07374/) (2019)
- [Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims](https://mlsanity.com/paper/2004.07213/) (2020)
- [Blockchained On-Device Federated Learning](https://mlsanity.com/paper/1808.03949/) (2018)
- [Reliable Federated Learning for Mobile Networks](https://mlsanity.com/paper/1910.06837/) (2019)
- [GTG-Shapley: Efficient and Accurate Participant Contribution Evaluation in Federated Learning](https://mlsanity.com/paper/2109.02053/) (2021)
- [SafetyNets: Verifiable Execution of Deep Neural Networks on an Untrusted Cloud](https://mlsanity.com/paper/1706.10268/) (2017)
- [A Reputation Mechanism Is All You Need: Collaborative Fairness and Adversarial Robustness in Federated Learning](https://mlsanity.com/paper/2011.10464/) (2020)
- [VFL: A Verifiable Federated Learning with Privacy-Preserving for Big Data in Industrial IoT](https://mlsanity.com/paper/2007.13585/) (2020)
- [A multi-component framework for the analysis and design of explainable artificial intelligence](https://mlsanity.com/paper/2005.01908/) (2020)
- [A Contract Theory based Incentive Mechanism for Federated Learning](https://mlsanity.com/paper/2108.05568/) (2021)
- [Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices](https://mlsanity.com/paper/1906.10893/) (2019)
- [Blockchain-based Trustworthy Federated Learning Architecture](https://mlsanity.com/paper/2108.06912/) (2021)

## Built on by (most cited first, 11 of 11)

- [Decentralized Federated Learning: A Survey on Security and Privacy](https://mlsanity.com/paper/2401.17319/) (2024)
- [Verification of Machine Unlearning is Fragile](https://mlsanity.com/paper/2408.00929/) (2024)
- [Zero-Knowledge Proof-based Verifiable Decentralized Machine Learning in Communication Network: A Comprehensive Survey](https://mlsanity.com/paper/2310.14848/) (2023)
- [Client-specific Property Inference against Secure Aggregation in Federated Learning](https://mlsanity.com/paper/2303.03908/) (2023)
- [Adaptive Coded Federated Learning: Privacy Preservation and Straggler Mitigation](https://mlsanity.com/paper/2403.14905/) (2024)
- [TrustChain: A Blockchain Framework for Auditing and Verifying Aggregators in Decentralized Federated Learning](https://mlsanity.com/paper/2502.16406/) (2025)
- [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)
- [Gaming and Cooperation in Federated Learning: What Can Happen and How to Monitor It](https://mlsanity.com/paper/2509.02391/) (2025)
- [zkUnlearner: A Zero-Knowledge Framework for Verifiable Unlearning with Multi-Granularity and Forgery-Resistance](https://mlsanity.com/paper/2509.07290/) (2025)
- [Shadow Unlearning: A Neuro-Semantic Approach to Fidelity-Preserving Faceless Forgetting in LLMs](https://mlsanity.com/paper/2601.04275/) (2026)

## Abstract (arXiv)

Federated learning (FL) is an emerging paradigm of collaborative machine learning that preserves user privacy while building powerful models. Nevertheless, due to the nature of open participation by self-interested entities, it needs to guard against potential misbehaviours by legitimate FL participants. FL verification techniques are promising solutions for this problem. They have been shown to effectively enhance the reliability of FL networks and help build trust among participants. Verifiable federated learning has become an emerging topic of research that has attracted significant interest from the academia and the industry alike. Currently, there is no comprehensive survey on the field of verifiable federated learning, which is interdisciplinary in nature and can be challenging for researchers to enter into. In this paper, we bridge this gap by reviewing works focusing on verifiable FL. We propose a novel taxonomy for verifiable FL covering both centralised and decentralised FL settings, summarise the commonly adopted performance evaluation approaches, and discuss promising directions towards a versatile verifiable FL framework.