Compressive Sensing, Federated Learning, Data privacy
Abstract
Compressed sensing, as an important signal processing technology, uses the sparsity or structure of the signal to reconstruct the original signal with samples much lower than the traditional sampling rate. Federated learning (FL), as a distributed machine learning method that allows model training without sharing sensitive data, provides an effective way to share knowledge across devices and organizations. Combining compressed sensing with federated learning has potential synergistic advantages, which can not only achieve efficient information extraction and transmission, but also protect personal privacy data. This paper aims to explore how compressed sensing technology can be applied to federated learning to accelerate the model training process, improve model accuracy, and ensure data privacy. By deeply studying this field, this paper reveals the potential applications, challenges and future development directions of compressed sensing in the federated learning environment, and promotes further innovation and application in the field of federated learning. Through comprehensive experiments and analysis, this paper provides insights into the integration of compressed sensing within federated learning frameworks, highlighting its role in optimizing communication efficiency and resource utilization.
