mlsanityData to 7 Oct 2026

EdgeFLow: Serverless Federated Learning via Sequential Model Migration in Edge Networks

arXiv 2603.02562 ↗First posted 3 Mar 2026cs.LGcited by 1

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Abstract (arXiv)

Federated Learning (FL) has emerged as a transformative distributed learning paradigm in the era of Internet of Things (IoT), reconceptualizing data processing methodologies. However, FL systems face significant communication bottlenecks due to inevitable client-server data exchanges and long-distance transmissions. This work presents EdgeFLow, an innovative FL framework that redesigns the system topology by replacing traditional cloud servers with sequential model migration between edge base stations. By conducting model aggregation and propagation exclusively at edge clusters, EdgeFLow eliminates cloud-based transmissions and substantially reduces global communication overhead. We provide rigorous convergence analysis for EdgeFLow under non-convex objectives and non-IID data distributions, extending classical FL convergence theory. Experimental results across various configurations validate the theoretical analysis, demonstrating that EdgeFLow achieves comparable accuracy improvements while significantly reducing communication costs. As a systemic architectural innovation for communication-efficient FL, EdgeFLow establishes a foundational framework for future developments in IoT and edge-network learning systems.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 15 of its 20 references that are in the map. First posted on arXiv 3 Mar 2026.

Builds on

15 of 20 references.

References · 15

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2016Overcoming catastrophic forgetting in neural networkscited by 4,718
  3. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  4. 2019FedMD: Heterogenous Federated Learning via Model Distillationcited by 454
  5. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 347
  6. 2018Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learningcited by 277
  7. 2019Model Pruning Enables Efficient Federated Learning on Edge Devicescited by 219
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  9. 2019First Analysis of Local GD on Heterogeneous Datacited by 80
  10. 2021Hierarchical Federated Learning with Quantization: Convergence Analysis and System Designcited by 47
  11. 2023Convergence Analysis of Sequential Federated Learning on Heterogeneous Datacited by 23
  12. 2023FedLP: Layer-wise Pruning Mechanism for Communication-Computation Efficient Federated Learningcited by 7
3 more
  1. 2022ISFL: Federated Learning for Non-i.i.d. Data with Local Importance Samplingcited by 4
  2. 2023FedNC: A Secure and Efficient Federated Learning Method with Network Codingcited by 2
  3. 2024Sequential Federated Learning in Hierarchical Architecture on Non-IID Datasetscited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Federated Client Selection under Partial Visibility: A POMDP Approach with Spatio-Temporal Attentioncited by 0