Integrate fast F1 FHE into Fate, from paper, "F1: A Fast and Programmable Accelerator for Fully Homomorphic Encryption"

[roboswell]

Fully Homomorphic Encryption (FHE) is extremely slow and thus limits adopting it by the wider Federated Learning community FHE slows down FATE (and all other federated learning frameworks) by a severe rate.

Let's Finally Devise a Way to Speed up FHE. One Solution - The F1 Accelerator The paper, "F1: A Fast and Programmable Accelerator for Fully Homomorphic Encryption" by Axel Feldmann et al. (2021) shows how F1 accelerates FHE and "outperforms state-of-the-art software implementations by gmean 5,400× and by up to 17,000× [and that] these speedups counter most of FHE’s overheads and enable new applications," (pg. 1).

Additional context Though the F1 paper doesn't discuss FHE in the context of Federated Learning, wouldn't it be possible to implement a similar system into the FATE framework to finally begin to resolve the slow speed problem that plagues federated learning?

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