BlockDFL: A Blockchain-based Fully Decentralized Peer-to-Peer Federated Learning Framework

Author:

Qin Zhen1ORCID,Yan Xueqiang2ORCID,Zhou Mengchu3ORCID,Deng Shuiguang1ORCID

Affiliation:

1. College of Computer Science and Technology, Zhejiang University, Hangzhou, China

2. Huawei Technologies Co. Ltd, Shanghai, China

3. Sussex Artificial Intelligence Institute, Zhejiang Gongshang University, Hangzhou, China

Funder

National Science Foundation of China

Key Research Project of Zhejiang Province

Publisher

ACM

Reference54 articles.

1. Integration of blockchain and federated learning for Internet of Things: Recent advances and future challenges

2. Poster

3. Peva Blanchard, El Mahdi El Mhamdi, Rachid Guerraoui, and Julien Stainer. 2017. Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent. In Annual Conference on Neural Information Processing Systems. 119--129.

4. Sebastian Caldas, Sai Meher Karthik Duddu, Peter Wu, Tian Li, Jakub Konevc nỳ, H Brendan McMahan, Virginia Smith, and Ameet Talwalkar. 2018. Leaf: A benchmark for federated settings. arXiv preprint arXiv:1812.01097 (2018).

5. Miguel Castro and Barbara Liskov. 1999. Practical Byzantine Fault Tolerance. In USENIX Symposium on Operating Systems Design and Implementation. 173--186.

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