With the development of distributed machine learning, Federated Learning (FL) and Split Learning (SL) stand out for their privacy-centric and computationally efficient approaches to leveraging decentralized data. These paradigms enable model training across multiple nodes without exchanging raw data. However, their operational frameworks differ, warranting a unified benchmark to systematically compare their efficacy. This project presents an evaluative benchmarking suite that assesses FL and SL across key performance metrics, including accuracy, communication costs, computational load, scalability, and robustness against adversarial threats. Employing diverse datasets from image classification to NLP tasks, and various neural architectures, the study offers a comprehensive analysis of both paradigms under different data conditions, including challenging non-IID distributions. Our experiments reveal the nuanced trade-offs between FL and SL: FL excels in heterogeneous data scenarios, while SL can minimize communication requirements and expedite convergence, sometimes at a slight accuracy trade-off. Significantly, we probe the paradigms' computational demands in relation to model complexity and dataset size, underscoring the necessity for adaptive, application-specific strategies. The security dimension is also explored, assessing each paradigm’s defenses against potential adversarial attacks, thus gauging their deployment readiness in sensitive domains. This benchmarking suite serves a dual purpose: it aids practitioners in paradigm selection by providing empirical performance metrics and assists researchers in standardizing the evaluation of new algorithms within these frameworks. In essence, our work offers a distilled comparison between FL and SL, equipping stakeholders with critical insights for deploying distributed learning systems and spurring further advancements in efficient, secure, and scalable machine learning methodologies.