Dynamic Computing First Network Multi-dimensional Resource Collaborative Allocation Based on Federated Segmentation Learning

Driven by the challenges associated with training large models in heterogeneous networks, where a one-size-fits-all training strategy can result in inefficiencies, we propose a versatile Federated Split Learning (FSL) algorithm designed for dynamic resource allocation. Conventional methods often encounter resource limitations, particularly in settings with varying computational abilities. Our algorithm overcomes this issue by intelligently distributing model components among nodes according to their specific computational capabilities, thereby enhancing resource utilization.A key feature of our approach is the inclusion of an auxiliary classifier layer that enables each client to calculate losses and locally update parameters, while only sending activation values to the central server. This mechanism not only reduces communication overhead but also speeds up the overall training process. Additionally, we implement an earlyexit inference strategy that adapts based on task complexity and available resources, further enhancing inference efficiency.