Octopus: A Robust and Privacy-Preserving Scheme for Compressed Gradients in Federated Learning

Summary

The paper proposes Octopus, a scheme for federated learning that compresses gradients with Sketch and embeds masks in the compressed gradients to protect privacy while reducing communication overhead. It adds an anti-disconnection strategy so model updates continue when some clients drop out. The authors report security and convergence analyses and performance evaluations showing robustness, stability, and efficiency over existing schemes.

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