mlsanityData to 14 Oct 2026

AdapterFL: Adaptive Heterogeneous Federated Learning for Resource-constrained Mobile Computing Systems

arXiv 2311.14037 ↗First posted 23 Nov 2023cs.LGcited by 4

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Abstract (arXiv)

Federated Learning (FL) enables collaborative learning of large-scale distributed clients without data sharing. However, due to the disparity of computing resources among massive mobile computing devices, the performance of traditional homogeneous model-based Federated Learning (FL) is seriously limited. On the one hand, to achieve model training in all the diverse clients, mobile computing systems can only use small low-performance models for collaborative learning. On the other hand, devices with high computing resources cannot train a high-performance large model with their insufficient raw data. To address the resource-constrained problem in mobile computing systems, we present a novel heterogeneous FL approach named AdapterFL, which uses a model reassemble strategy to facilitate collaborative training of massive heterogeneous mobile devices adaptively. Specifically, we select multiple candidate heterogeneous models based on the computing performance of massive mobile devices and then divide each heterogeneous model into two partitions. By reassembling the partitions, we can generate models with varied sizes that are combined by the partial parameters of the large model with the partial parameters of the small model. Using these reassembled models for FL training, we can train the partial parameters of the large model using low-performance devices. In this way, we can alleviate performance degradation in large models due to resource constraints. The experimental results show that AdapterFL can achieve up to 12% accuracy improvement compared to the state-of-the-art heterogeneous federated learning methods in resource-constrained scenarios.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 34 of its 54 references that are in the map. First posted on arXiv 23 Nov 2023.

Builds on

34 of 54 references.

References · 34

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  3. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  4. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  5. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,795
  6. 2019Parameter-Efficient Transfer Learning for NLPcited by 4,236
  7. 2019Advances and Open Problems in Federated Learningcited by 3,046
  8. 2019Similarity of Neural Network Representations Revisitedcited by 1,755
  9. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,538
  10. 2015Learning Transferable Features with Deep Adaptation Networkscited by 1,329
  11. 2014How transferable are features in deep neural networks?cited by 1,225
  12. 2018Federated Learning with Non-IID Datacited by 1,108
22 more
  1. 2020Adaptive Federated Optimizationcited by 1,046
  2. 2016An overview of gradient descent optimization algorithmscited by 1,033
  3. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 851
  4. 2020Ensemble Distillation for Robust Model Fusion in Federated Learningcited by 682
  5. 2019FedMD: Heterogenous Federated Learning via Model Distillationcited by 459
  6. 2017Efficient Processing of Deep Neural Networks: A Tutorial and Surveycited by 434
  7. 2021Data-Free Knowledge Distillation for Heterogeneous Federated Learningcited by 383
  8. 2020HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clientscited by 337
  9. 2016EIE: Efficient Inference Engine on Compressed Deep Neural Networkcited by 295
  10. 2020Distillation-Based Semi-Supervised Federated Learning for Communication-Efficient Collaborative Training with Non-IID Private Datacited by 125
  11. 2021Asynchronous Federated Learning on Heterogeneous Devices: A Surveycited by 93
  12. 2022Heterogeneous Ensemble Knowledge Transfer for Training Large Models in Federated Learningcited by 81
  13. 2019DELTA: DEep Learning Transfer using Feature Map with Attention for Convolutional Networkscited by 80
  14. 2022Deep Model Reassemblycited by 78
  15. 2022No One Left Behind: Inclusive Federated Learning over Heterogeneous Devicescited by 37
  16. 2022Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customizationcited by 35
  17. 2016Challenges and Opportunities in Edge Computingcited by 26
  18. 2020Transfer Learning or Self-supervised Learning? A Tale of Two Pretraining Paradigmscited by 23
  19. 2023Distillation from Heterogeneous Models for Top-K Recommendationcited by 13
  20. 2022GitFL: Adaptive Asynchronous Federated Learning using Version Controlcited by 10
  21. 2022To Store or Not? Online Data Selection for Federated Learning with Limited Storagecited by 7
  22. 2022HierarchyFL: Heterogeneous Federated Learning via Hierarchical Self-Distillationcited by 2

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Citing papers per year: 2024: 2; 2025: 2.

4 papers.

  1. 2024CaBaFL: Asynchronous Federated Learning via Hierarchical Cache and Feature Balancecited by 2
  2. 2025FedADP: Unified Model Aggregation for Federated Learning with Heterogeneous Model Architecturescited by 2
  3. 2024Architectural Blueprint For Heterogeneity-Resilient Federated Learningcited by 0
  4. 2025QA-HFL: Quality-Aware Hierarchical Federated Learning for Resource-Constrained Mobile Devices with Heterogeneous Image Qualitycited by 0