Adaptive Decision-Making Framework for Federated Learning Tasks in Multi-Tier Computing

Employing federated learning (FL) in multi-tier computing to achieve various intelligent services is widely in demand. However, adaptive decision-making of FL tasks to improve latency performance is still mostly limited to theoretical studies of local computational optimality, and is challenging to carry out in practical systems. This paper proposes an adaptive decision-making framework (ADMF) for FL tasks with multilayer computational participation to attain lower latency with a global optimization perspective. In this demo, a prototype framework of ADMF in multi-tier computing is demonstrated. First, the feasibility of implementing the proposed framework is provided. Then, we show the latency performance through the experimental results that validate the practicality and effectiveness of the proposed framework.