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Pushing Forward Multi-Secret-Key Homomorphic Encryption for Private Average Aggregation

arXiv · AI, language, vision and robotics · article · Sep 1, 2026 · UTC

Federated Learning enables multiple clients to train a shared model while keeping their local datasets isolated. However, the exchanged model updates may still leak sensitive information, making private aggregation a central building block in practical deployments, especially in the cross-silo setting. Homomorphic Encryption naturally fits the client--aggregator communication pattern of Federated Learning, but conventional single-key deployments rely on strong non-collusion assumptions. Multiparty Homomorphic Encryption removes this limitation, although recent attacks under restricted decrypti

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