Platform Design for Privacy-Preserving Federated Learning using Homomorphic Encryption : Wild-and-Crazy-Idea Paper

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Abstract
Federated learning (FL) has been increasingly widely used for distributed and privacy-preserving machine learning (ML) environments, as the raw training data can stay local to clients while leveraging model updates from individual clients. Homomorphic encryption (HE) technologies can provide additional privacy protection for FL by encrypting the model update parameters while allowing model aggregation on a remote server. Although HE-enabled FL seems to be a promising privacy-preserving ML solution, it requires significantly more computational and memory resources, requiring a dedicated hardware and software platform. In this paper, we discuss preliminary but concrete and realizable research ideas for analyzing the requirements for HE-enabled FL and for designing a hardware and software platform. Furthermore, we propose a platform co-design process that considers various design stages and challenges in the platform co-design.

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