mlsanityData to 14 Oct 2026

Multi-Edge Server-Assisted Dynamic Federated Learning with an Optimized Floating Aggregation Point

arXiv 2203.13950 ↗First posted 26 Mar 2022cs.LG cs.NIcited by 10

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Abstract (arXiv)

We propose cooperative edge-assisted dynamic federated learning (CE-FL). CE-FL introduces a distributed machine learning (ML) architecture, where data collection is carried out at the end devices, while the model training is conducted cooperatively at the end devices and the edge servers, enabled via data offloading from the end devices to the edge servers through base stations. CE-FL also introduces floating aggregation point, where the local models generated at the devices and the servers are aggregated at an edge server, which varies from one model training round to another to cope with the network evolution in terms of data distribution and users' mobility. CE-FL considers the heterogeneity of network elements in terms of communication/computation models and the proximity to one another. CE-FL further presumes a dynamic environment with online variation of data at the network devices which causes a drift at the ML model performance. We model the processes taken during CE-FL, and conduct analytical convergence analysis of its ML model training. We then formulate network-aware CE-FL which aims to adaptively optimize all the network elements via tuning their contribution to the learning process, which turns out to be a non-convex mixed integer problem. Motivated by the large scale of the system, we propose a distributed optimization solver to break down the computation of the solution across the network elements. We finally demonstrate the effectiveness of our framework with the data collected from a real-world testbed.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 38 of its 81 references that are in the map. First posted on arXiv 26 Mar 2022.

Builds on

38 of 81 references.

References · 38

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26 more
  1. 2019Client-Edge-Cloud Hierarchical Federated Learningcited by 202
  2. 2019On the Convergence of Local Descent Methods in Federated Learningcited by 153
  3. 2020UVeQFed: Universal Vector Quantization for Federated Learningcited by 107
  4. 2019Hierarchical Federated Learning Across Heterogeneous Cellular Networkscited by 98
  5. 2019Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocationcited by 94
  6. 2021Semi-Decentralized Federated Learning with Cooperative D2D Local Model Aggregationscited by 51
  7. 2020Wireless Communications for Collaborative Federated Learningcited by 47
  8. 2020Multi-Stage Hybrid Federated Learning over Large-Scale D2D-Enabled Fog Networkscited by 42
  9. 2021Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementationcited by 40
  10. 2020Federated Learning via Intelligent Reflecting Surfacecited by 38
  11. 2019Robust Federated Learning with Noisy Communicationcited by 34
  12. 2020Demystifying Why Local Aggregation Helps: Convergence Analysis of Hierarchical SGDcited by 26
  13. 2021Time-Correlated Sparsification for Communication-Efficient Federated Learningcited by 26
  14. 2019Cell-Free Massive MIMO for Wireless Federated Learningcited by 25
  15. 2021UAV-assisted Online Machine Learning over Multi-Tiered Networks: A Hierarchical Nested Personalized Federated Learning Approachcited by 20
  16. 2022Parallel Successive Learning for Dynamic Distributed Model Training over Heterogeneous Wireless Networkscited by 18
  17. 20206G White Paper on Machine Learning in Wireless Communication Networkscited by 14
  18. 2020Self-organizing Democratized Learning: Towards Large-scale Distributed Learning Systemscited by 14
  19. 2022PerFED-GAN: Personalized Federated Learning via Generative Adversarial Networkscited by 14
  20. 2018Principled Deep Neural Network Training through Linear Programmingcited by 13
  21. 2020Dynamic Federated Learningcited by 12
  22. 2016Parallel and Distributed Methods for Nonconvex Optimization--Part II: Applicationscited by 10
  23. 2020Can we Generalize and Distribute Private Representation Learning?cited by 5
  24. 2021On-the-fly Resource-Aware Model Aggregation for Federated Learning in Heterogeneous Edgecited by 2
  25. 2018Power-Aware Allocation of Graph Jobs in Geo-Distributed Cloud Networkscited by 1
  26. 2019Multi-tier Caching Analysis in CDN-based Over-the-top Video Streaming Systemscited by 1

Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 5; 2024: 3; 2025: 1.

10 papers.

  1. 2022Online Federated Learning via Non-Stationary Detection and Adaptation amidst Concept Driftcited by 10
  2. 2023Towards Cooperative Federated Learning over Heterogeneous Edge/Fog Networkscited by 8
  3. 2023FilFL: Client Filtering for Optimized Client Participation in Federated Learningcited by 5
  4. 2023Cooperative Federated Learning over Ground-to-Satellite Integrated Networks: Joint Local Computation and Data Offloadingcited by 5
  5. 2023Asynchronous Multi-Model Dynamic Federated Learning over Wireless Networks: Theory, Modeling, and Optimizationcited by 3
  6. 2024Dynamic D2D-Assisted Federated Learning over O-RAN: Performance Analysis, MAC Scheduler, and Asymmetric User Selectioncited by 3
  7. 2023Device Sampling and Resource Optimization for Federated Learning in Cooperative Edge Networkscited by 2
  8. 2025Incentivizing Multi-Tenant Split Federated Learning for Foundation Models at the Network Edgecited by 1
  9. 2024Towards Client Driven Federated Learningcited by 0
  10. 2024Twin Sorting Dynamic Programming Assisted User Association and Wireless Bandwidth Allocation for Hierarchical Federated Learningcited by 0