Asynchronous Multi-Model Dynamic Federated Learning over Wireless Networks: Theory, Modeling, and Optimization

no code implementations • 22 May 2023 • Zhan-Lun Chang, Seyyedali Hosseinalipour, Mung Chiang, Christopher G. Brinton

Our analysis sheds light on the joint impact of device training variables (e. g., number of local gradient descent steps), asynchronous scheduling decisions (i. e., when a device trains a task), and dynamic data drifts on the performance of ML training for different tasks.

Federated Learning Scheduling