  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.

Cryptography and Security;Machine Learning;Optimization and Control