# Survey on the Convergence of Machine Learning and Blockchain

arXiv: https://arxiv.org/abs/2201.00976 · HTML: https://mlsanity.com/paper/2201.00976/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 38 references that are in the map.
- First posted on arXiv 4 Jan 2022.

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

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Lip Reading Sentences in the Wild](https://mlsanity.com/paper/1611.05358/) (2016)
- [IPFS - Content Addressed, Versioned, P2P File System](https://mlsanity.com/paper/1407.3561/) (2014)
- [Achieving Human Parity in Conversational Speech Recognition](https://mlsanity.com/paper/1610.05256/) (2016)
- [Decentralized & Collaborative AI on Blockchain](https://mlsanity.com/paper/1907.07247/) (2019)
- [Trustless Machine Learning Contracts; Evaluating and Exchanging Machine Learning Models on the Ethereum Blockchain](https://mlsanity.com/paper/1802.10185/) (2018)

## Built on by (most cited first, 4 of 4)

- [eFedLLM: Efficient LLM Inference Based on Federated Learning](https://mlsanity.com/paper/2411.16003/) (2024)
- [An Investigation of Smart Contract for Collaborative Machine Learning Model Training](https://mlsanity.com/paper/2209.05017/) (2022)
- [mathsfVISTA: Decentralized Machine Learning in Adversary Dominated Environments](https://mlsanity.com/paper/2605.07841/) (2026)
- [Learning from Acceptance: Cumulative Regret in the Game of Coding](https://mlsanity.com/paper/2605.09754/) (2026)

## Abstract (arXiv)

Machine learning (ML) has been pervasively researched nowadays and it has been applied in many aspects of real life. Nevertheless, issues of model and data still accompany the development of ML. For instance, training of traditional ML models is limited to the access of data sets, which are generally proprietary; published ML models may soon be out of date without an update of new data and continuous training; malicious data contributors may upload wrongly labeled data that leads to undesirable training results; and the abuse of private data and data leakage also exit. With the utilization of blockchain, an emerging and swiftly developing technology, these problems can be efficiently solved. In this paper, we survey the convergence of collaborative ML and blockchain. Different ways of the combination of these two technologies are investigated and their fields of application are examined. Discussion on the limitations of current research and their future directions are also included.