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Reducing ciphertext expansion and online costs in Federated Learning could revolutionize how sensitive data is aggregated without compromising privacy.
Smudging noise with large variance can mitigate security vulnerabilities in threshold HE schemes for federated learning, but at a performance cost that needs careful consideration.
