# Smart Policy Control for Securing Federated Learning Management System

arXiv: https://arxiv.org/abs/2305.09134 · HTML: https://mlsanity.com/paper/2305.09134/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 36 references that are in the map.
- First posted on arXiv 16 May 2023.

## Builds on (most cited first, 7 of 7)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [Gazelle: A Low Latency Framework for Secure Neural Network Inference](https://mlsanity.com/paper/1801.05507/) (2018)
- [Federated Learning Meets Blockchain in Edge Computing: Opportunities and Challenges](https://mlsanity.com/paper/2104.01776/) (2021)
- [Blockchain-Federated-Learning and Deep Learning Models for COVID-19 detection using CT Imaging](https://mlsanity.com/paper/2007.06537/) (2020)
- [Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Review](https://mlsanity.com/paper/2205.07855/) (2022)

## Built on by (most cited first, 8 of 8)

- [Graph Canvas for Controllable 3D Scene Generation](https://mlsanity.com/paper/2412.00091/) (2024)
- [FLEDGE: Ledger-based Federated Learning Resilient to Inference and Backdoor Attacks](https://mlsanity.com/paper/2310.02113/) (2023)
- [Blockchain-Enabled Privacy-Preserving Second-Order Federated Edge Learning in Personalized Healthcare](https://mlsanity.com/paper/2506.00416/) (2025)
- [Federated Learning based Latent Factorization of Tensors for Privacy-Preserving QoS Prediction](https://mlsanity.com/paper/2407.19828/) (2024)
- [Causal Reasoning Elicits Controllable 3D Scene Generation](https://mlsanity.com/paper/2509.15249/) (2025)
- [ICBAC: an Intelligent Contract-Based Access Control framework for supply chain management by integrating blockchain and federated learning](https://mlsanity.com/paper/2602.08014/) (2026)
- [FLFL: Federated Latent Factor Learning for Private Recovery of Spatio-Temporal Signals](https://mlsanity.com/paper/2606.23091/) (2026)
- [A Lifecycle Cost Analysis of Smart-Contract-Coordinated Federated Learning Marketplaces](https://mlsanity.com/paper/2609.13170/) (2026)

## Abstract (arXiv)

The widespread adoption of Internet of Things (IoT) devices in smart cities, intelligent healthcare systems, and various real-world applications have resulted in the generation of vast amounts of data, often analyzed using different Machine Learning (ML) models. Federated learning (FL) has been acknowledged as a privacy-preserving machine learning technology, where multiple parties cooperatively train ML models without exchanging raw data. However, the current FL architecture does not allow for an audit of the training process due to the various data-protection policies implemented by each FL participant. Furthermore, there is no global model verifiability available in the current architecture. This paper proposes a smart contract-based policy control for securing the Federated Learning (FL) management system. First, we develop and deploy a smart contract-based local training policy control on the FL participants' side. This policy control is used to verify the training process, ensuring that the evaluation process follows the same rules for all FL participants. We then enforce a smart contract-based aggregation policy to manage the global model aggregation process. Upon completion, the aggregated model and policy are stored on blockchain-based storage. Subsequently, we distribute the aggregated global model and the smart contract to all FL participants. Our proposed method uses smart policy control to manage access and verify the integrity of machine learning models. We conducted multiple experiments with various machine learning architectures and datasets to evaluate our proposed framework, such as MNIST and CIFAR-10.