# Trustworthy DNN Partition for Blockchain-enabled Digital Twin in Wireless IIoT Networks

arXiv: https://arxiv.org/abs/2405.17914 · HTML: https://mlsanity.com/paper/2405.17914/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 43 references that are in the map.
- First posted on arXiv 28 May 2024.

## Builds on (most cited first, 10 of 10)

- [CoEdge: Cooperative DNN Inference with Adaptive Workload Partitioning over Heterogeneous Edge Devices](https://mlsanity.com/paper/2012.03257/) (2020)
- [Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocation](https://mlsanity.com/paper/2101.06905/) (2021)
- [Joint Trajectory and Communication Design for Multi-UAV Enabled Wireless Networks](https://mlsanity.com/paper/1705.02723/) (2017)
- [Low-latency Federated Learning and Blockchain for Edge Association in Digital Twin empowered 6G Networks](https://mlsanity.com/paper/2011.09902/) (2020)
- [Low-latency Federated Learning with DNN Partition in Distributed Industrial IoT Networks](https://mlsanity.com/paper/2210.14700/) (2022)
- [Blockchain Assisted Federated Learning over Wireless Channels: Dynamic Resource Allocation and Client Scheduling](https://mlsanity.com/paper/2105.14708/) (2021)
- [Federated Learning with Nesterov Accelerated Gradient](https://mlsanity.com/paper/2009.08716/) (2020)
- [Digital Twin-based 3D Map Management for Edge-assisted Device Pose Tracking in Mobile AR](https://mlsanity.com/paper/2311.04997/) (2023)
- [Pooling is not Favorable: Decentralize Mining Power of PoW Blockchain Using Age-of-Work](https://mlsanity.com/paper/2104.01918/) (2021)
- [Energy-Efficient Cooperative Secure Transmission in Multi-UAV Enabled Wireless Networks](https://mlsanity.com/paper/1912.01995/) (2019)

## Built on by (most cited first, 1 of 1)

- [Energy-Efficient Federated Learning and Migration in Digital Twin Edge Networks](https://mlsanity.com/paper/2503.15822/) (2025)

## Abstract (arXiv)

Digital twin (DT) has emerged as a promising solution to enhance manufacturing efficiency in industrial Internet of Things (IIoT) networks. To promote the efficiency and trustworthiness of DT for wireless IIoT networks, we propose a blockchain-enabled DT (B-DT) framework that employs deep neural network (DNN) partitioning technique and reputation-based consensus mechanism, wherein the DTs maintained at the gateway side execute DNN inference tasks using the data collected from their associated IIoT devices. First, we employ DNN partitioning technique to offload the top-layer DNN inference tasks to the access point (AP) side, which alleviates the computation burden at the gateway side and thereby improves the efficiency of DNN inference. Second, we propose a reputation-based consensus mechanism that integrates Proof of Work (PoW) and Proof of Stake (PoS). Specifically, the proposed consensus mechanism evaluates the off-chain reputation of each AP according to its computation resource contributions to the DNN inference tasks, and utilizes the off-chain reputation as a stake to adjust the block generation difficulty. Third, we formulate a stochastic optimization problem of communication resource (i.e., partition point) and computation resource allocation (i.e., computation frequency of APs for top-layer DNN inference and block generation) to minimize system latency under the time-varying channel state and long-term constraints of off-chain reputation, and solve the problem using Lyapunov optimization method. Experimental results show that the proposed dynamic DNN partitioning and resource allocation (DPRA) algorithm outperforms the baselines in terms of reducing the overall latency while guaranteeing the trustworthiness of the B-DT system.