mlsanityData to 7 Oct 2026

AdaptiveFL: Adaptive Heterogeneous Federated Learning for Resource-Constrained AIoT Systems

arXiv 2311.13166 ↗First posted 22 Nov 2023cs.LG cs.DCcited by 9

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Abstract (arXiv)

Although Federated Learning (FL) is promising to enable collaborative learning among Artificial Intelligence of Things (AIoT) devices, it suffers from the problem of low classification performance due to various heterogeneity factors (e.g., computing capacity, memory size) of devices and uncertain operating environments. To address these issues, this paper introduces an effective FL approach named AdaptiveFL based on a novel fine-grained width-wise model pruning strategy, which can generate various heterogeneous local models for heterogeneous AIoT devices. By using our proposed reinforcement learning-based device selection mechanism, AdaptiveFL can adaptively dispatch suitable heterogeneous models to corresponding AIoT devices on the fly based on their available resources for local training. Experimental results show that, compared to state-of-the-art methods, AdaptiveFL can achieve up to 16.83% inference improvements for both IID and non-IID scenarios.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 12 of its 20 references that are in the map. First posted on arXiv 22 Nov 2023.

Builds on

12 of 20 references.

References · 12

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  3. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  4. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,773
  5. 2016Pruning Filters for Efficient ConvNetscited by 1,129
  6. 2018LEAF: A Benchmark for Federated Settingscited by 780
  7. 2020Ensemble Distillation for Robust Model Fusion in Federated Learningcited by 678
  8. 2016Unifying Count-Based Exploration and Intrinsic Motivationcited by 632
  9. 2020HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clientscited by 334
  10. 2022Heterogeneous Ensemble Knowledge Transfer for Training Large Models in Federated Learningcited by 81
  11. 2022GitFL: Adaptive Asynchronous Federated Learning using Version Controlcited by 10
  12. 2023FedAIoT: A Federated Learning Benchmark for Artificial Intelligence of Thingscited by 8

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 4; 2026: 1.

9 papers.

  1. 2024CaBaFL: Asynchronous Federated Learning via Hierarchical Cache and Feature Balancecited by 2
  2. 2024EFTViT: Efficient Federated Training of Vision Transformers with Masked Images on Resource-Constrained Clientscited by 2
  3. 2024Learnable Sparse Customization in Heterogeneous Edge Computingcited by 2
  4. 2025Energy and Memory-Efficient Federated Learning With Ordered Layer Freezingcited by 1
  5. 2026SubFLOT: Submodel Extraction for Efficient and Personalized Federated Learning via Optimal Transportcited by 1
  6. 2024FedQP: Towards Accurate Federated Learning using Quadratic Programming Guided Mutationcited by 0
  7. 2025Accelerated Training on Low-Power Edge Devicescited by 0
  8. 2025QA-HFL: Quality-Aware Hierarchical Federated Learning for Resource-Constrained Mobile Devices with Heterogeneous Image Qualitycited by 0
  9. 2025Gradients as an Action: Towards Communication-Efficient Federated Recommender Systems via Adaptive Action Sharingcited by 0