Enhancing Efficiency and Privacy in Distributed Machine Learning: A Comparative Analysis of Federated Learning and Split Learning Techniques

As the volume of data generated by individuals and organizations continues its exponential grow, the need for efficient and secure Machine Learning (ML) algorithms has become increasingly important. Federated Learning (FL) and Split Learning (SL) are two innovative approaches designed to tackle these issues by allowing ML models to be trained using decentralized data sources while preserving data privacy. In this paper, we present a comparative analysis of FL and SL, focusing on their strengths and weaknesses in terms of accuracy, efficiency, and security. We also discuss their potential applications in various domains. Our analysis provides insights into the trade-offs between these two techniques and helps researchers and practitioners make informed decisions when choosing the appropriate approach for their specific use case.