FLock: Defending Malicious Behaviors in Federated Learning with Blockchain

Description

This is the code base for FLock's system design paper.

A short version of this paper has been accepted at NeurIPS 2022 workshop: Decentralization and Trustworthy Machine Learning in Web3: Methodologies, Platforms, and Applications

You can check the full paper here.

Codes

Detailed system design codes and experiments will be published later.

Citation

We welcome efforts from both AI and Blockchain communities to expand this promising domain of work, please cite our work as follows:

@misc{https://doi.org/10.48550/arxiv.2211.04344,
  doi = {10.48550/ARXIV.2211.04344},
  
  url = {https://arxiv.org/abs/2211.04344},
  
  author = {Dong, Nanqing and Sun, Jiahao and Wang, Zhipeng and Zhang, Shuoying and Zheng, Shuhao},
  
  keywords = {Cryptography and Security (cs.CR), Artificial Intelligence (cs.AI), Computer Science and Game Theory (cs.GT), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {FLock: Defending Malicious Behaviors in Federated Learning with Blockchain},
  
  publisher = {arXiv},
  
  year = {2022},
  
  copyright = {Creative Commons Attribution Non Commercial No Derivatives 4.0 International}
}

Future works

A detailed paper with data, formula, and experiments will be published soon after this.

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