mlsanityData to 7 Oct 2026

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

arXiv 2509.07342 ↗First posted 9 Sep 2025cs.LG cs.DCcited by 1

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 27 of its 40 references that are in the map. First posted on arXiv 9 Sep 2025.

Builds on

27 of 40 references.

References · 27

  1. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  2. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  3. 2023A Comprehensive Survey of Continual Learning: Theory, Method and Applicationcited by 860
  4. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 407
  5. 2020Communication-Efficient Edge AI: Algorithms and Systemscited by 95
  6. 2020Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learningcited by 90
  7. 2015Speeding Up Distributed Machine Learning Using Codescited by 76
  8. 2020Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edgecited by 71
  9. 2023A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learningcited by 66
  10. 2020Scheduling for Cellular Federated Edge Learning with Importance and Channel Awarenesscited by 62
  11. 2020Straggler-Resilient Federated Learning: Leveraging the Interplay Between Statistical Accuracy and System Heterogeneitycited by 56
  12. 2023Recent Advances on Federated Learning: A Systematic Surveycited by 40
15 more
  1. 2021Non-IID data and Continual Learning processes in Federated Learning: A long road aheadcited by 36
  2. 2022Federated Learning under Distributed Concept Driftcited by 35
  3. 2021Dynamic Scheduling for Over-the-Air Federated Edge Learning with Energy Constraintscited by 34
  4. 2024Towards Efficient Replay in Federated Incremental Learningcited by 33
  5. 2022Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reductioncited by 32
  6. 2022Rethinking Data Heterogeneity in Federated Learning: Introducing a New Notion and Standard Benchmarkscited by 24
  7. 2024Adaptive Memory Replay for Continual Learningcited by 21
  8. 2024Rehearsal-Free Continual Federated Learning with Synergistic Synaptic Intelligencecited by 15
  9. 2021Asynchronous Federated Learning for Sensor Data with Concept Driftcited by 14
  10. 2025Federated Continual Learning: Concepts, Challenges, and Solutionscited by 13
  11. 2024Advances in Robust Federated Learning: A Survey with Heterogeneity Considerationscited by 12
  12. 2024Mobility Accelerates Learning: Convergence Analysis on Hierarchical Federated Learning in Vehicular Networkscited by 11
  13. 2024Federated Continual Learning for Edge-AI: A Comprehensive Surveycited by 10
  14. 2016A Unified Coding Framework for Distributed Computing with Straggling Serverscited by 8
  15. 2025FedCGD: Collective Gradient Divergence Optimized Scheduling for Wireless Federated Learningcited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Sylvas: Synergistic Learning Value based Device Scheduling in Federated Continual Learningcited by 0