Time Minimization in Hierarchical Federated Learning
This work addresses efficiency issues in hierarchical federated learning systems, which is incremental as it builds on existing methods to reduce delays.
The paper tackles the problem of communication and computation delays in hierarchical federated learning by optimizing local and edge iteration counts and UE-to-edge associations, resulting in minimized latency and faster global model convergence as shown in simulations.
Federated Learning is a modern decentralized machine learning technique where user equipments perform machine learning tasks locally and then upload the model parameters to a central server. In this paper, we consider a 3-layer hierarchical federated learning system which involves model parameter exchanges between the cloud and edge servers, and the edge servers and user equipment. In a hierarchical federated learning model, delay in communication and computation of model parameters has a great impact on achieving a predefined global model accuracy. Therefore, we formulate a joint learning and communication optimization problem to minimize total model parameter communication and computation delay, by optimizing local iteration counts and edge iteration counts. To solve the problem, an iterative algorithm is proposed. After that, a time-minimized UE-to-edge association algorithm is presented where the maximum latency of the system is reduced. Simulation results show that the global model converges faster under optimal edge server and local iteration counts. The hierarchical federated learning latency is minimized with the proposed UE-to-edge association strategy.