StreamSplit: Decoupling Split Federated Learning for Asynchronous and Continuous Optimization

Distributed learning enables collaborative model training across distributed nodes. Split Federated Learning (SFL), a form of distributed learning, partitions model computation between clients and a server, but its tightly coupled execution requires clients and the server to wait for gradients and features from each other for subsequent optimization. This bidirectional dependency causes frequent execution stalls and severe resource underutilization, particularly under heterogeneous environments, leading to low training efficiency. In this paper, we propose StreamSplit, a decoupled SFL framework that enables clients and the server to optimize asynchronously and continuously while maintaining consistency between them. StreamSplit introduces a lightweight Shadow Server-side Model (SSM) at each client to substitute for the server-side model during local updates, enabling clients to perform continuous training without waiting for gradients. We design a depth-decoupled parallel SSM architecture with a corresponding multi-depth alignment strategy, allowing the SSM to approximate the evolving server-side model and provide reliable surrogate gradients for client updates. Meanwhile, StreamSplit maintains a Latest Feature Table to store and provide candidate features for continuous server-side model training. We also introduce an optimization-aware feature selection strategy to select informative features that facilitate server-side model training and SSM alignment. Experiments show that StreamSplit improves training efficiency and scalability over existing SFL methods under heterogeneous environments. It also performs well on both convolutional and transformer-based models, demonstrating its applicability across model architectures.