MASER combines majority-vote weight pruning with multi-key homomorphic encryption to reduce privacy-preserving federated learning overhead by 3 to 8 times while keeping accuracy within about 1 percent of vanilla FL.
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Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption
MASER combines majority-vote weight pruning with multi-key homomorphic encryption to reduce privacy-preserving federated learning overhead by 3 to 8 times while keeping accuracy within about 1 percent of vanilla FL.