# A proof of contribution in blockchain using game theoretical deep learning model

arXiv: https://arxiv.org/abs/2409.07460 · HTML: https://mlsanity.com/paper/2409.07460/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 31 references that are in the map.
- First posted on arXiv 25 Aug 2024.

## Builds on (most cited first, 13 of 13)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [DARTS: Differentiable Architecture Search](https://mlsanity.com/paper/1806.09055/) (2018)
- [Knowledge Distillation: A Survey](https://mlsanity.com/paper/2006.05525/) (2020)
- [Efficient Neural Architecture Search via Parameter Sharing](https://mlsanity.com/paper/1802.03268/) (2018)
- [Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization](https://mlsanity.com/paper/1603.06560/) (2016)
- [Challenges of Real-World Reinforcement Learning](https://mlsanity.com/paper/1904.12901/) (2019)
- [Hierarchical Transformers for Multi-Document Summarization](https://mlsanity.com/paper/1905.13164/) (2019)
- [A Survey on the Security of Blockchain Systems](https://mlsanity.com/paper/1802.06993/) (2018)
- [KnowRU: Knowledge Reusing via Knowledge Distillation in Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2103.14891/) (2021)
- [Iterative Refinement for Machine Translation](https://mlsanity.com/paper/1610.06602/) (2016)
- [A Game-Theoretic Analysis of Shard-Based Permissionless Blockchains](https://mlsanity.com/paper/1809.07307/) (2018)
- [Feasibility of Proof of Authority as a Consensus Protocol Model](https://mlsanity.com/paper/2109.02480/) (2021)
- [Quantum learning and essential cognition under the traction of meta-characteristics in an open world](https://mlsanity.com/paper/2311.13335/) (2023)

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

- [Achieving Trustworthy Real-Time Decision Support Systems with Low-Latency Interpretable AI Models](https://mlsanity.com/paper/2506.20018/) (2025)

## Abstract (arXiv)

Building elastic and scalable edge resources is an inevitable prerequisite for providing platform-based smart city services. Smart city services are delivered through edge computing to provide low-latency applications. However, edge computing has always faced the challenge of limited resources. A single edge device cannot undertake the various intelligent computations in a smart city, and the large-scale deployment of edge devices from different service providers to build an edge resource platform has become a necessity. Selecting computing power from different service providers is a game-theoretic problem. To incentivize service providers to actively contribute their valuable resources and provide low-latency collaborative computing power, we introduce a game-theoretic deep learning model to reach a consensus among service providers on task scheduling and resource provisioning. Traditional centralized resource management approaches are inefficient and lack credibility, while the introduction of blockchain technology can enable decentralized resource trading and scheduling. We propose a contribution-based proof mechanism to provide the low-latency service of edge computing. The deep learning model consists of dual encoders and a single decoder, where the GNN (Graph Neural Network) encoder processes structured decision action data, and the RNN (Recurrent Neural Network) encoder handles time-series task scheduling data. Extensive experiments have demonstrated that our model reduces latency by 584% compared to the state-of-the-art.