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Award ID contains: 1816399

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  1. ; ; (, 2023 IEEE International Conference on Quantum Computing and Engineering (QCE))
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  2. ; ; ; (, 2023 IEEE International Conference on Quantum Software (QSW))
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  3. ; ; ; (, IEEE Transactions on Cloud Computing)
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  4. ; ; ; ; ; ; (, IEEE Transactions on Computers)
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  5. ; ; (, 17th International Conference on Mobility, Sensing and Networking (MSN))
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  6. ; ; ; ; ; (, Computer networks)
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  7. ; ; (, 2021 IEEE/ACM 29th International Symposium on Quality of Service (IWQoS))
    null (Ed.)
    Accepted Manuscript Full Text Available
  8. ; ; ; (, ACM/IEEE Symposium on Edge Computing)
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  9. ; ; ; (, IEEE Transactions on Mobile Computing)
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  10. ; ; ; (, IJCAI)
    Many times, training a large scale deep learning neural network on a single machine becomes more and more difficult for a complex network model. Distributed training provides an efficient solution, but Byzantine attacks may occur on participating workers. They may be compromised or suffer from hardware failures. If they upload poisonous gradients, the training will become unstable or even converge to a saddle point. In this paper, we propose FABA, a Fast Aggregation algorithm against Byzantine Attacks, which removes the outliers in the uploaded gradients and obtains gradients that are close to the true gradients. We show the convergence of our algorithm. The experiments demonstrate that our algorithm can achieve similar performance to non-Byzantine case and higher efficiency as compared to previous algorithms. 
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