FedOptimus: Optimizing Vertical Federated Learning for Scalability and Efficiency

Federated learning (FL) is a collaborative machine learning paradigm which ensures data privacy by training models across distributed datasets without centralizing sensitive information. Vertical Federated Learning (VFL), a kind of FL training method, facilitates collaboration among participants with each client having received a different feature space of a shared user set. VFL thus, proves invaluable in privacy-sensitive domains such as finance and healthcare. Despite its inherent advantages, VFL faced challenges including communication bottlenecks, computational inefficiency, and slow convergence due to non-IID data distributions. This paper introduces FedOptimus, a robust Multi-VFL framework integrating advanced techniques for improved model efficiency and scalability. FedOptimus leverages a Mutual Information (MI)-based client selection to prioritize high-contribution participants, reducing computational overhead. Further, it incorporates server-side momentum techniques like FedAvgM and SLOWMO to stabilize updates and accelerate convergence on heterogeneous data. Additionally, performing K-Step Averaging minimizes communication costs while maintaining model performance. FedOptimus proves to be superior in performance on benchmark datasets such as CIFAR-10, MNIST, and FMNIST, showcasing its scalability and effectiveness in real-world multi-server, multi-client settings. By unifying advanced optimization methods, FedOptimus sets a new standard for efficient and scalable Vertical Federated Learning frameworks, paving the way for broader adoption in complex, privacy-sensitive domains.