FedTeddi: Temporal Drift and Divergence Aware Scheduling for Timely Federated Edge Learning
This addresses timely model adaptation for distributed clients in federated edge learning with dynamic data, representing an incremental improvement over existing scheduling methods.
The paper tackles the problem of adapting federated edge learning models to evolving, non-i.i.d. data over time by proposing FedTeddi, a scheduling algorithm that improves convergence rates by 58.4% on CIFAR-10 and 49.2% on CIFAR-100 compared to random scheduling.
Federated edge learning (FEEL) enables collaborative model training across distributed clients over wireless networks without exposing raw data. While most existing studies assume static datasets, in real-world scenarios clients may continuously collect data with time-varying and non-independent and identically distributed (non-i.i.d.) characteristics. A critical challenge is how to adapt models in a timely yet efficient manner to such evolving data. In this paper, we propose FedTeddi, a temporal-drift-and-divergence-aware scheduling algorithm that facilitates fast convergence of FEEL under dynamic data evolution and communication resource limits. We first quantify the temporal dynamics and non-i.i.d. characteristics of data using temporal drift and collective divergence, respectively, and represent them as the Earth Mover's Distance (EMD) of class distributions for classification tasks. We then propose a novel optimization objective and develop a joint scheduling and bandwidth allocation algorithm, enabling the FEEL system to learn from new data quickly without forgetting previous knowledge. Experimental results show that our algorithm achieves higher test accuracy and faster convergence compared to benchmark methods, improving the rate of convergence by 58.4% on CIFAR-10 and 49.2% on CIFAR-100 compared to random scheduling.