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QA-HFL: Quality-Aware Hierarchical Federated Learning for Resource-Constrained Mobile Devices with Heterogeneous Image Quality

arXiv 2506.05411 ↗First posted 4 Jun 2025cs.CR cs.CVcited by 0

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Abstract (arXiv)

This paper introduces QA-HFL, a quality-aware hierarchical federated learning framework that efficiently handles heterogeneous image quality across resource-constrained mobile devices. Our approach trains specialized local models for different image quality levels and aggregates their features using a quality-weighted fusion mechanism, while incorporating differential privacy protection. Experiments on MNIST demonstrate that QA-HFL achieves 92.31% accuracy after just three federation rounds, significantly outperforming state-of-the-art methods like FedRolex (86.42%). Under strict privacy constraints, our approach maintains 30.77% accuracy with formal differential privacy guarantees. Counter-intuitively, low-end devices contributed most significantly (63.5%) to the final model despite using 100 fewer parameters than high-end counterparts. Our quality-aware approach addresses accuracy decline through device-specific regularization, adaptive weighting, intelligent client selection, and server-side knowledge distillation, while maintaining efficient communication with a 4.71% compression ratio. Statistical analysis confirms that our approach significantly outperforms baseline methods (p 0.01) under both standard and privacy-constrained conditions.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 27 references that are in the map. First posted on arXiv 4 Jun 2025.

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References · 7

  1. 2022FedRolex: Model-Heterogeneous Federated Learning with Rolling Sub-Model Extractioncited by 102
  2. 2021FedHM: Efficient Federated Learning for Heterogeneous Models via Low-rank Factorizationcited by 31
  3. 2023AdaptiveFL: Adaptive Heterogeneous Federated Learning for Resource-Constrained AIoT Systemscited by 9
  4. 2023AdapterFL: Adaptive Heterogeneous Federated Learning for Resource-constrained Mobile Computing Systemscited by 4
  5. 2024HierSFL: Local Differential Privacy-aided Split Federated Learning in Mobile Edge Computingcited by 3
  6. 2022HierarchyFL: Heterogeneous Federated Learning via Hierarchical Self-Distillationcited by 2
  7. 2023ECLM: Efficient Edge-Cloud Collaborative Learning with Continuous Environment Adaptationcited by 1

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  1. 2022Towards Understanding Quality Challenges of the Federated Learning for Neural Networks: A First Look from the Lens of Robustness
  2. 2026Quantization in Federated Learning: Methods, Challenges and Future Directions
  3. 2021PPFL: Privacy-preserving Federated Learning with Trusted Execution Environments
  4. 2025A Generalized Hierarchical Federated Learning Framework with Theoretical Guarantees
  5. 2026FedLTLib: A Comprehensive Benchmark for Federated Long-Tail Learning
  6. 2025Towards a Larger Model via One-Shot Federated Learning on Heterogeneous Client Models
  7. 2023Fed-QSSL: A Framework for Personalized Federated Learning under Bitwidth and Data Heterogeneity
  8. 2026Enhanced Privacy and Communication Efficiency in Non-IID Federated Learning with Adaptive Quantization and Differential Privacy