# Decentralized and Robust Privacy-Preserving Model Using Blockchain-Enabled Federated Deep Learning in Intelligent Enterprises

arXiv: https://arxiv.org/abs/2502.17485 · HTML: https://mlsanity.com/paper/2502.17485/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Feb 2025.

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

- [A Lightweight and Secure Deep Learning Model for Privacy-Preserving Federated Learning in Intelligent Enterprises](https://mlsanity.com/paper/2503.02017/) (2025)
- [Communication-Efficient Personalized Distributed Learning with Data and Node Heterogeneity](https://mlsanity.com/paper/2504.17520/) (2025)
- [ZTFed-MAS2S: A Zero-Trust Federated Learning Framework with Verifiable Privacy and Trust-Aware Aggregation for Wind Power Data Imputation](https://mlsanity.com/paper/2508.18318/) (2025)
- [Zero-Shot Visual Deepfake Detection: Can AI Predict and Prevent Fake Content Before It's Created?](https://mlsanity.com/paper/2509.18461/) (2025)
- [Fractional-Order Federated Learning](https://mlsanity.com/paper/2602.15380/) (2026)
- [TITAN-FedAnil+: Trust-Based Adaptive Blockchain Federated Learning for Resource-Constrained Intelligent Enterprises](https://mlsanity.com/paper/2606.04388/) (2026)
- [FedImp: Enhancing Federated Learning Convergence with Impurity-Based Weighting](https://mlsanity.com/paper/2608.14654/) (2026)

## Abstract (arXiv)

In Federated Deep Learning (FDL), multiple local enterprises are allowed to train a model jointly. Then, they submit their local updates to the central server, and the server aggregates the updates to create a global model. However, trained models usually perform worse than centralized models, especially when the training data distribution is non-independent and identically distributed (nonIID). NonIID data harms the accuracy and performance of the model. Additionally, due to the centrality of federated learning (FL) and the untrustworthiness of enterprises, traditional FL solutions are vulnerable to security and privacy attacks. To tackle this issue, we propose FedAnil, a secure blockchain enabled Federated Deep Learning Model that improves enterprise models decentralization, performance, and tamper proof properties, incorporating two main phases. The first phase addresses the nonIID challenge (label and feature distribution skew). The second phase addresses security and privacy concerns against poisoning and inference attacks through three steps. Extensive experiments were conducted using the Sent140, FashionMNIST, FEMNIST, and CIFAR10 new real world datasets to evaluate FedAnils robustness and performance. The simulation results demonstrate that FedAnil satisfies FDL privacy preserving requirements. In terms of convergence analysis, the model parameter obtained with FedAnil converges to the optimum of the model parameter. In addition, it performs better in terms of accuracy (more than 11, 15, and 24%) and computation overhead (less than 8, 10, and 15%) compared with baseline approaches, namely ShieldFL, RVPFL, and RFA.