# VFL: A Verifiable Federated Learning with Privacy-Preserving for Big Data in Industrial IoT

arXiv: https://arxiv.org/abs/2007.13585 · HTML: https://mlsanity.com/paper/2007.13585/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jul 2020.

## Built on by (most cited first, 18 of 18)

- [Towards Verifiable Federated Learning](https://mlsanity.com/paper/2202.08310/) (2022)
- [Client-specific Property Inference against Secure Aggregation in Federated Learning](https://mlsanity.com/paper/2303.03908/) (2023)
- [Federated Learning for Big Data: A Survey on Opportunities, Applications, and Future Directions](https://mlsanity.com/paper/2110.04160/) (2021)
- [SafeNet: The Unreasonable Effectiveness of Ensembles in Private Collaborative Learning](https://mlsanity.com/paper/2205.09986/) (2022)
- [Trustworthy Federated Learning: A Survey](https://mlsanity.com/paper/2305.11537/) (2023)
- [Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights](https://mlsanity.com/paper/2312.04688/) (2023)
- [Linkage on Security, Privacy and Fairness in Federated Learning: New Balances and New Perspectives](https://mlsanity.com/paper/2406.10884/) (2024)
- [Privacy Threats and Countermeasures in Federated Learning for Internet of Things: A Systematic Review](https://mlsanity.com/paper/2407.18096/) (2024)
- [Towards Dataset Copyright Evasion Attack against Personalized Text-to-Image Diffusion Models](https://mlsanity.com/paper/2505.02824/) (2025)
- [SPRITE: A Scalable Privacy-Preserving and Verifiable Collaborative Learning for Industrial IoT](https://mlsanity.com/paper/2203.11914/) (2022)
- [LabObf: A Label Protection Scheme for Vertical Federated Learning Through Label Obfuscation](https://mlsanity.com/paper/2405.17042/) (2024)
- [A Lightweight Privacy-Preserving Scheme Using Label-based Pixel Block Mixing for Image Classification in Deep Learning](https://mlsanity.com/paper/2105.08876/) (2021)
- [Blockchain-based Optimized Client Selection and Privacy Preserved Framework for Federated Learning](https://mlsanity.com/paper/2308.04442/) (2023)
- [Federated Learning based Latent Factorization of Tensors for Privacy-Preserving QoS Prediction](https://mlsanity.com/paper/2407.19828/) (2024)
- [A Multivocal Literature Review on Privacy and Fairness in Federated Learning](https://mlsanity.com/paper/2408.08666/) (2024)
- [SoK: Verifiable Cross-Silo FL](https://mlsanity.com/paper/2410.09124/) (2024)
- [Repurposing Backdoors for Good: Ephemeral Intrinsic Proofs for Verifiable Aggregation in Cross-silo Federated Learning](https://mlsanity.com/paper/2603.10692/) (2026)
- [Client-Verifiable and Efficient Federated Unlearning in Low-Altitude Wireless Networks](https://mlsanity.com/paper/2603.29688/) (2026)

## Abstract (arXiv)

Due to the strong analytical ability of big data, deep learning has been widely applied to train the collected data in industrial IoT. However, for privacy issues, traditional data-gathering centralized learning is not applicable to industrial scenarios sensitive to training sets. Recently, federated learning has received widespread attention, since it trains a model by only relying on gradient aggregation without accessing training sets. But existing researches reveal that the shared gradient still retains the sensitive information of the training set. Even worse, a malicious aggregation server may return forged aggregated gradients. In this paper, we propose the VFL, verifiable federated learning with privacy-preserving for big data in industrial IoT. Specifically, we use Lagrange interpolation to elaborately set interpolation points for verifying the correctness of the aggregated gradients. Compared with existing schemes, the verification overhead of VFL remains constant regardless of the number of participants. Moreover, we employ the blinding technology to protect the privacy of the gradients submitted by the participants. If no more than n-2 of n participants collude with the aggregation server, VFL could guarantee the encrypted gradients of other participants not being inverted. Experimental evaluations corroborate the practical performance of the presented VFL framework with high accuracy and efficiency.