mlsanityData to 7 Oct 2026

Architectural Blueprint For Heterogeneity-Resilient Federated Learning

arXiv 2403.04546 ↗First posted 7 Mar 2024cs.LG cs.DC cs.NIcited by 0

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Abstract (arXiv)

This paper proposes a novel three tier architecture for federated learning to optimize edge computing environments. The proposed architecture addresses the challenges associated with client data heterogeneity and computational constraints. It introduces a scalable, privacy preserving framework that enhances the efficiency of distributed machine learning. Through experimentation, the paper demonstrates the architecture capability to manage non IID data sets more effectively than traditional federated learning models. Additionally, the paper highlights the potential of this innovative approach to significantly improve model accuracy, reduce communication overhead, and facilitate broader adoption of federated learning technologies.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 19 of its 36 references that are in the map. First posted on arXiv 7 Mar 2024.

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References · 19

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Advances and Open Problems in Federated Learningcited by 3,021
  3. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  4. 2020Personalized Federated Learning with Moreau Envelopescited by 612
  5. 2021Towards Personalized Federated Learningcited by 462
  6. 2020Think Locally, Act Globally: Federated Learning with Local and Global Representationscited by 322
  7. 2020Three Approaches for Personalization with Applications to Federated Learningcited by 321
  8. 2020Federated learning with hierarchical clustering of local updates to improve training on non-IID datacited by 222
  9. 2021Personalized Federated Learning using Hypernetworkscited by 210
  10. 2022Federated Learning with Partial Model Personalizationcited by 114
  11. 2021Parameterized Knowledge Transfer for Personalized Federated Learningcited by 108
  12. 2019Robust Federated Learning in a Heterogeneous Environmentcited by 88
7 more
  1. 2021Personalized Federated Learning with Gaussian Processescited by 76
  2. 2019Patient Clustering Improves Efficiency of Federated Machine Learning to predict mortality and hospital stay time using distributed Electronic Medical Recordscited by 70
  3. 2021QuPeD: Quantized Personalization via Distillation with Applications to Federated Learningcited by 32
  4. 2022Towards Federated Clustering: A Federated Fuzzy c-Means Algorithm (FFCM)cited by 29
  5. 2020Dynamic Clustering in Federated Learningcited by 27
  6. 2023AdapterFL: Adaptive Heterogeneous Federated Learning for Resource-constrained Mobile Computing Systemscited by 4
  7. 2024Federated Learning for Iot/Edge/Fog Computing Systemscited by 1

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