Randomized Quantization for Privacy in Resource Constrained Machine Learning at-the-Edge and Federated Learning

Randomized Quantization for Privacy in Resource Constrained Machine Learning at-the-Edge and Federated Learning

The increasing adoption of machine learning at the edge (ML-at-the-edge) and federated learning (FL) presents a dual challenge: ensuring data privacy as well as addressing resource constraints such as limited computational power, memory, and communication bandwidth. Traditional approaches typically...

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Bibliographic Details
Main Authors: Ce Feng, Parv Venkitasubramaniam
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Transactions on Machine Learning in Communications and Networking
Subjects:
Privacy
quantization
ML-at-the-edge
differential privacy
federated learning
Online Access:https://ieeexplore.ieee.org/document/10919124/
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