What question did this study set out to answer?

The aim is to review federated learning architectures that maintain data privacy in distributed machine learning.

April 22, 2026Open Access

Federated Learning Architectures For Privacy-Preserving Distributed Machine Learning

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This review analyzes federated learning architectures to enhance privacy in distributed machine learning systems.

The aim is to review federated learning architectures that maintain data privacy in distributed machine learning.
Examined federated learning frameworks and client-server coordination models.
Reviewed optimization strategies and secure aggregation mechanisms.
Analyzed alternative distributed learning paradigms such as split learning.
Identified key architectural patterns for federated learning implementations.
Demonstrated the convergence behavior of FedProx under diverse data distributions.
Highlighted ongoing challenges in communication efficiency and privacy leakage.

Adam Richards (2023) studied this question.

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