The paper serves as a tutorial on Distributed Privacy-preserving Federated Learning (DPFL), presenting a thorough review of experimental methods. It discusses the capabilities and limitations of current DPFL approaches and addresses the scarcity of empirical research in existing literature. By conducting a numerical evaluation, the authors analyze the generalizability and applicability of various methods, thus providing both theoretical and practical insights into DPFL. This work enhances understanding by linking theoretical concepts to real-world implementations, making it a valuable resource for advancing the field.
This paper aims to offer a more holistic understanding of Distributed Privacy-preserving Federated Learning (DPFL) by bridging the gap between theoretical insights and empirical results.
We comprehensively evaluate existing DPFL methods to uncover their capabilities and limitations, thereby enhancing our understanding of their practical applications in real-world scenarios.
#distributed-learning #federated-learning #privacy-preservation #machine-learning #statistical-methods
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