An Efficient Architecture for Edge AI Federated Learning With Homomorphic Encryption

An Efficient Architecture for Edge AI Federated Learning With Homomorphic Encryption

With the rapid growth of edge AI applications, there is an increasing demand for federated learning (FL) frameworks that are both efficient and privacy-preserving. This work introduces a robust approach that leverages homomorphic encryption (HE) to ensure data confidentiality during decentralized tr...

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Bibliographic Details
Main Authors: Dadmehr Rahbari, Masoud Daneshtalab, Maksim Jenihhin
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Federated learning
homomorphic encryption
efficiency
security
edge AI
Online Access:https://ieeexplore.ieee.org/document/11023593/
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