mlsanityData to 7 Oct 2026

Agglomerative Federated Learning: Empowering Larger Model Training via End-Edge-Cloud Collaboration

arXiv 2312.11489 ↗First posted 1 Dec 2023cs.DC cs.LGcited by 10

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Abstract (arXiv)

Federated Learning (FL) enables training Artificial Intelligence (AI) models over end devices without compromising their privacy. As computing tasks are increasingly performed by a combination of cloud, edge, and end devices, FL can benefit from this End-Edge-Cloud Collaboration (EECC) paradigm to achieve collaborative device-scale expansion with real-time access. Although Hierarchical Federated Learning (HFL) supports multi-tier model aggregation suitable for EECC, prior works assume the same model structure on all computing nodes, constraining the model scale by the weakest end devices. To address this issue, we propose Agglomerative Federated Learning (FedAgg), which is a novel EECC-empowered FL framework that allows the trained models from end, edge, to cloud to grow larger in size and stronger in generalization ability. FedAgg recursively organizes computing nodes among all tiers based on Bridge Sample Based Online Distillation Protocol (BSBODP), which enables every pair of parent-child computing nodes to mutually transfer and distill knowledge extracted from generated bridge samples. This design enhances the performance by exploiting the potential of larger models, with privacy constraints of FL and flexibility requirements of EECC both satisfied. Experiments under various settings demonstrate that FedAgg outperforms state-of-the-art methods by an average of 4.53% accuracy gains and remarkable improvements in convergence rate.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 22 of its 32 references that are in the map. First posted on arXiv 1 Dec 2023.

Builds on

22 of 32 references.

References · 22

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2019Advances and Open Problems in Federated Learningcited by 3,021
  4. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  5. 2019Federated Machine Learning: Concept and Applicationscited by 409
  6. 2020Knowledge Distillation and Student-Teacher Learning for Visual Intelligence: A Review and New Outlookscited by 312
  7. 2020FedML: A Research Library and Benchmark for Federated Machine Learningcited by 298
  8. 2021Federated Learning for Internet of Things: A Comprehensive Surveycited by 247
  9. 2019Client-Edge-Cloud Hierarchical Federated Learningcited by 202
  10. 2018Large scale distributed neural network training through online distillationcited by 144
  11. 2022FedRolex: Model-Heterogeneous Federated Learning with Rolling Sub-Model Extractioncited by 102
  12. 2022Heterogeneous Ensemble Knowledge Transfer for Training Large Models in Federated Learningcited by 81
10 more
  1. 2021Model Complexity of Deep Learning: A Surveycited by 64
  2. 2021FedGEMS: Federated Learning of Larger Server Models via Selective Knowledge Fusioncited by 49
  3. 2021Federated Learning for Internet of Things: Applications, Challenges, and Opportunitiescited by 49
  4. 2021Mobility-Aware Cluster Federated Learning in Hierarchical Wireless Networkscited by 35
  5. 2021Towards Model Agnostic Federated Learning Using Knowledge Distillationcited by 31
  6. 2020Federated Edge Learning : Design Issues and Challengescited by 26
  7. 2022Exploring the Distributed Knowledge Congruence in Proxy-data-free Federated Distillationcited by 22
  8. 2020Self-organizing Democratized Learning: Towards Large-scale Distributed Learning Systemscited by 14
  9. 2020Distributed and Democratized Learning: Philosophy and Research Challengescited by 6
  10. 2022FedDCT: Federated Learning of Large Convolutional Neural Networks on Resource Constrained Devices using Divide and Collaborative Trainingcited by 3

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Cited by year

Citing papers per year: 2024: 5; 2025: 5.

10 papers.

  1. 2024Hierarchical Split Federated Learning: Convergence Analysis and System Optimizationcited by 38
  2. 2025Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Surveycited by 13
  3. 2025FNBench: Benchmarking Robust Federated Learning against Noisy Labelscited by 6
  4. 2024Federated Class-Incremental Learning with New-Class Augmented Self-Distillationcited by 4
  5. 2025Federated Intelligence: When Large AI Models Meet Federated Fine-Tuning and Collaborative Reasoning at the Network Edgecited by 4
  6. 2025Recursive Offloading for LLM Serving in Multi-tier Networkscited by 2
  7. 2024Logits Poisoning Attack in Federated Distillationcited by 1
  8. 2024FedCache 2.0: Federated Edge Learning with Knowledge Caching and Dataset Distillationcited by 1
  9. 2024Peak-Controlled Logits Poisoning Attack in Federated Distillationcited by 1
  10. 2025Beyond Model Scale Limits: End-Edge-Cloud Federated Learning with Self-Rectified Knowledge Agglomerationcited by 1