OrbiVFL: System-Algorithm Co-Design for Asynchronous Vertical Federated Learning

Vertical Federated Learning (VFL) enables multiple parties with vertically partitioned features to collaboratively train models without directly sharing private data. However, existing VFL systems often suffer from substantial communication and synchronization overhead under heterogeneous computation and wide-area networks, while existing asynchronous approaches insufficiently exploit worker-level parallelism within each party. We present OrbiVFL, a system-algorithm co-design framework for practical asynchronous VFL. OrbiVFL builds on Ray and the parameter-server architecture to decouple computation and communication across workers and parties, while explicitly aligning embeddings and gradients through unique identifiers. To reduce communication-induced stalls, we derive inter-party local update intervals and design a semi-asynchronous synchronization mechanism for heterogeneous workers within each party. We further provide convergence guarantees for the proposed training scheme. We evaluate OrbiVFL on four representative datasets, including UCIHAR, Mini-ImageNet, VisionTouch, and MuJoCo. Compared with state-of-the-art VFL approaches, OrbiVFL reduces end-to-end training time by up to 76.40% while maintaining comparable or higher model accuracy, and improves computational resource utilization by 76.35%. Extensive ablation studies further demonstrate the effectiveness and robustness of our design. Finally, we deploy OrbiVFL in a production-oriented embodied-intelligence scenario, where it achieves 26.30% training-time reduction without sacrificing accuracy under realistic computation.