Corpus ID: 219981321

Byzantine-Resilient High-Dimensional SGD with Local Iterations on Heterogeneous Data

@article{Data2020ByzantineResilientHS,
  title={Byzantine-Resilient High-Dimensional SGD with Local Iterations on Heterogeneous Data},
  author={Deepesh Data and S. Diggavi},
  journal={ArXiv},
  year={2020},
  volume={abs/2006.13041}
}
We study stochastic gradient descent (SGD) with local iterations in the presence of malicious/Byzantine clients, motivated by the federated learning. The clients, instead of communicating with the central server in every iteration, maintain their local models, which they update by taking several SGD iterations based on their own datasets and then communicate the net update with the server, thereby achieving communication-efficiency. Furthermore, only a subset of clients communicate with the… Expand
1 Citations
Data Encoding for Byzantine-Resilient Distributed Optimization

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