Traditional AI methodologies necessitate centralized data collection, which
becomes impractical when facing problems with network communication, data
privacy, or storage capacity. Federated Learning (FL) offers a paradigm that
empowers distributed AI model training without collecting raw data. There are
different choices for providing privacy during FL training. One of the popular
methodologies is employing Homomorphic Encryption (HE) - a breakthrough in
privacy-preserving computation from Cryptography. However, these methods have a
price in the form of extra computation and memory footprint. To resolve these
issues, we propose an innovative framework that synergizes permutation-based
compressors with Classical Cryptography, even though employing Classical
Cryptography was assumed to be impossible in the past in the context of FL. Our
framework offers a way to replace HE with cheaper Classical Cryptography
primitives which provides security for the training process. It fosters
asynchronous communication and provides flexible deployment options in various
communication topologies.

传统的人工智能方法需要集中的数据收集，而联邦学习（FL）提供了一种在不收集原始数据的情况下进行分布式人工智能模型训练的范例。本文提出了一种创新的框架，将基于排列的压缩机与传统密码学结合起来，用更便宜的传统密码学原语取代同态加密（HE），以提供训练过程的安全性，并促进异步通信和灵活的部署选项。