Network for Distributed Intelligence

Distributed Intelligence, such as federated learning and split learning, has attracted attention due to data privacy issues computational resource constraints. For distributed intelligence, it is essential to design efficient communication paths according to the learning and inference context. Service Function Chaining (SFC) is a networking technique that ensures traffic traverses a predefined sequence of service functions, realizing arbitrary network services through dynamic and efficient communication paths. Inspired by this concept, we propose an SFC-based architecture for Multi-hop Split Inference (MSI), where split sub-models are interpreted as service functions and their composition forms a service chain representing the global model. By leveraging SFC, the proposed architecture dynamically establishes communication paths for split sub-models, ensuring efficient and adaptive execution.