Search Results for author: Huawei Huang

Found 4 papers, 1 papers with code

A_Blockchain-Based_Decentralized_Federated_Learning_Framework_with_Committee_Consensus

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).

Federated Learning

A Blockchain-based Decentralized Federated Learning Framework with Committee Consensus

1 code implementation2 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).

Federated Learning

Prophet: Proactive Candidate-Selection for Federated Learning by Predicting the Qualities of Training and Reporting Phases

no code implementations3 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.

Federated Learning

PIRATE: A Blockchain-based Secure Framework of Distributed Machine Learning in 5G Networks

no code implementations17 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

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