no code implementations • IEEE Network 2021 • Yuzheng Li, Chuan Chen, Nan Liu, Huawei Huang, Zibin Zheng, and Qiang Yan
To address these security issues, we propose a decentralized federated learning framework based on blockchain, that is, a Blockchain- based Federated Learning framework with Committee consensus (BFLC).
1 code implementation • 2 Apr 2020 • Yuzheng Li, Chuan Chen, Nan Liu, Huawei Huang, Zibin Zheng, Qiang Yan
To address these security issues, we proposed a decentralized federated learning framework based on blockchain, i. e., a Blockchain-based Federated Learning framework with Committee consensus (BFLC).
no code implementations • 3 Feb 2020 • Huawei Huang, Kangying Lin, Song Guo, Pan Zhou, Zibin Zheng
In the dynamic environment, the mobile devices selected by the existing reactive candidate-selection algorithms very possibly fail to complete the training and reporting phases of FL, because the FL parameter server only knows the currently-observed resources of all candidates.
no code implementations • 17 Dec 2019 • Sicong Zhou, Huawei Huang, Wuhui Chen, Zibin Zheng, Song Guo
Therefore, to provide the byzantine-resilience for distributed learning in 5G era, this article proposes a secure computing framework based on the sharding-technique of blockchain, namely PIRATE.
Distributed, Parallel, and Cluster Computing Cryptography and Security