Collaborative Model Training for Wireless Networks

The patent merges multiple individual model training tasks into a single collaborative training process. Multiple wireless access network devices share common model layers that are trained once and reused across different tasks, while maintaining task-specific unique layers. This combining approach reduces redundant training computations and lowers overall training overhead while preserving task-specific accuracy.

The patent creates universal shared model layers that serve multiple different training tasks simultaneously. These shared layers capture common patterns and features that are applicable across various tasks, making the model structure multi-functional. Each wireless access network device can utilize these universal layers for its specific tasks without requiring separate complete model training.

The patent merges data processing operations by performing collaborative training where multiple devices contribute to shared model layers without requiring each device to upload complete datasets independently. This reduces the total volume of data transmitted across the network, thereby lowering data security risks while maintaining task-specific performance through the combination of shared and unique layers.

The patent segments the model into shared layers and unique layers, where shared layers are trained collaboratively with reduced data upload requirements, and unique layers handle task-specific computations locally. This segmentation allows task-specific performance to be maintained through unique layers while reducing overall data transmission needs through the shared layers.

The patent merges the training workload distribution by having multiple wireless access network devices collaboratively train shared model layers, rather than relying on the OAM entity to perform all training computations. This distributes the computational burden across multiple devices, improving resource allocation efficiency and reducing disproportionate OAM entity consumption while maintaining model specialization through task-specific unique layers.

The patent enables wireless access network devices to participate in model training themselves, rather than relying entirely on the OAM entity. Each device contributes to training shared layers and maintains its own unique layers, making the system self-sufficient and reducing the resource burden on the OAM entity while preserving specialized capabilities.