# TDLearning: Trusted Distributed Collaborative Learning Based on Blockchain Smart Contracts
###### Abstract  
Massive amounts of data drive the performance of deep learning models, but in practice, data resources are often highly dispersed and bound by data privacy and security concerns, making it difficult for multiple data sources to share their local data directly. Data resources are difficult to aggregate effectively, resulting in a lack of support for model training. How to collaborate between data sources in order to aggregate the value of data resources is therefore an important research question. However, existing distributed-collaborative-learning architectures still face serious challenges in collaborating between nodes that lack mutual trust, with security and trust issues seriously affecting the confidence and willingness of data sources to participate in collaboration. Blockchain technology provides trusted distributed storage and computing, and combining it with collaboration between data sources to build trusted distributed-collaborative-learning architectures is an extremely valuable research direction for application. We propose a trusted distributed-collaborative-learning mechanism based on blockchain smart contracts by the mechanism uses blockchain smart contracts to define and encapsulate collaborative behaviours, relationships and norms between distributed collaborative nodes. Secondly, we propose a model-fusion method based on feature fusion, which replaces the direct sharing of local data resources with distributed-model collaborative training and organises distributed data resources for distributed collaboration to improve model performance. Finally, in order to verify the trustworthiness and usability of the proposed mechanism, on the one hand, we implement formal modelling and verification of the smart contract by using Coloured Petri Net and prove that the mechanism satisfies the expected trustworthiness properties by verifying the formal model of the smart contract associated with the mechanism. On the other hand, the model-fusion method based on feature fusion is evaluated in different datasets and collaboration scenarios, while a typical collaborative-learning case is implemented for a comprehensive analysis and validation of the mechanism. The experimental results show that the proposed mechanism can provide a trusted and fair collaboration infrastructure for distributed-collaboration nodes that lack mutual trust and organise decentralised data resources for collaborative model training to develop effective global models. distributed collaborative learning; smart contract; model fusion; formal verification +
Footnote †: journal:  
that we focus more on the definition of collaborative behaviours, relationships and norms among distributed cooperative nodes through blockchain smart contracts. Based on this definition and transfer learning, we propose a feature-fusion-based model-fusion method. Similarly to their approach, we replace the direct sharing of local data resources with distributed-model collaboration training to organise scattered data resources for distributed cooperation, thereby improving model performance. However, our work does not involve an improvement in federated learning, and smart contracts play a more significant role in the collaboration process. However, in practical application scenarios, data resources are often highly dispersed. In addition, due to data privacy and security constraints, it is difficult for multiple data sources to share their local data directly. It is difficult to effectively aggregate data resources, which will lead to a lack of support for model training. This problem restricts the further improvement in the model performance. To address these challenges, distributed-collaborative-learning architectures based on dispersed data have emerged. For example, the federated learning (FL) proposed by Google [5], which implements an efficient distributed-collaboration model based on decentralised data and computing power.