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We propose Falcon, an end-to-end 3-party protocol for efficient privatetraining and inference of large machine learning models. Falcon presents fourmain advantages - (i) It is highly expressive with support for high capacitynetworks such as VGG16 (ii) it supports batch normalization which is importantfor training complex networks such as AlexNet (iii) Falcon guarantees securitywith abort against malicious adversaries, assuming an honest majority (iv)Lastly, Falcon presents new theoretical insights for protocol design that makeit highly efficient and allow it to outperform existing secure deep learningsolutions. Compared to prior art for private inference, we are about 8x fasterthan SecureNN (PETS'19) on average and comparable to ABY3 (CCS'18). We areabout 16-200x more communication efficient than either of these. For privatetraining, we are about 6x faster than SecureNN, 4.4x faster than ABY3 and about2-60x more communication efficient. Our experiments in the WAN setting showthat over large networks and datasets, compute operations dominate the overalllatency of MPC, as opposed to the communication.

FALCON: Honest-Majority Maliciously Secure Framework for Private Deep Learning