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Communication-Computation Efficient Secure Aggregation for Federated Learning
Through extensive real-world experiments, we demonstrate that our scheme, using only $20 \sim 30\%$ of the resources required in the conventional scheme, maintains virtually the same levels of reliability and data privacy in practical federated learning systems.
Election Coding for Distributed Learning: Protecting SignSGD against Byzantine Attacks
Recent advances in large-scale distributed learning algorithms have enabled communication-efficient training via SignSGD.
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