FedTeddi: Temporal Drift and Divergence Aware Scheduling for Timely Federated Edge Learning

• URL: http://arxiv.org/abs/2509.07342v1
• Date: Tue, 09 Sep 2025 02:33:48 GMT
• Title: FedTeddi: Temporal Drift and Divergence Aware Scheduling for Timely Federated Edge Learning
• Authors: Yuxuan Bai, Yuxuan Sun, Tan Chen, Wei Chen, Sheng Zhou, Zhisheng Niu,
• Abstract summary: Federated edge learning (FEEL) enables collaborative model training across distributed clients over wireless networks without exposing raw data.<n>A critical challenge is how to adapt models in a timely yet efficient manner to such evolving data.<n>We propose FedTeddi, a temporal-drift-and-divergence-aware scheduling algorithm that facilitates fast convergence of FEEL.
• Score: 12.104759384825705
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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