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

• URL: http://arxiv.org/abs/2506.05411v2
• Date: Sun, 21 Sep 2025 04:49:25 GMT
• Title: QA-HFL: Quality-Aware Hierarchical Federated Learning for Resource-Constrained Mobile Devices with Heterogeneous Image Quality
• Authors: Sajid Hussain, Muhammad Sohail, Nauman Ali Khan,
• Abstract summary: This paper introduces QA-HFL, a quality-aware hierarchical federated learning framework that efficiently handles heterogeneous image quality across resource-constrained mobile devices.<n>Our approach trains specialized local models for different image quality levels and aggregates their features using a quality-weighted fusion mechanism.<n> Experiments on MNIST demonstrate that QA-HFL achieves 92.31% accuracy after just three federation rounds.
• Score: 1.5612040984769855
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Black-Box Reliability Certification for AI Agents via Self-Consistency Sampling and Conformal Calibration [0.0]
  We answer with a reliability level -- a single number per system-task pair.<n>Self-consistency sampling reduces uncertainty exponentially.<n> conformal calibration guarantees correctness within 1/(n+1) of the target level.
  arXiv  Detail & Related papers  (2026-02-24T21:03:50Z)
• Fixed-Threshold Evaluation of a Hybrid CNN-ViT for AI-Generated Image Detection Across Photos and Art [0.0]
  We introduce fixed-threshold evaluation that holds decision thresholds, selected once on clean validation data, fixed across all post-processing transformations.<n>Our hybrid approach achieves balanced crossdomain performance: 91.4% accuracy on tinygenimage photos, 89.7% accuracy on AiData art/graphics, and 98.3% (competitive) on CIFAKE.
  arXiv  Detail & Related papers  (2025-12-25T05:19:09Z)
• Reliable LLM-Based Edge-Cloud-Expert Cascades for Telecom Knowledge Systems [54.916243942641444]
  Large language models (LLMs) are emerging as key enablers of automation in domains such as telecommunications.<n>We study an edge-cloud-expert cascaded LLM-based knowledge system that supports decision-making through a question-and-answer pipeline.
  arXiv  Detail & Related papers  (2025-12-23T03:10:09Z)
• Continual Action Quality Assessment via Adaptive Manifold-Aligned Graph Regularization [53.82400605816587]
  Action Quality Assessment (AQA) quantifies human actions in videos, supporting applications in sports scoring, rehabilitation, and skill evaluation.<n>A major challenge lies in the non-stationary nature of quality distributions in real-world scenarios.<n>We introduce Continual AQA (CAQA), which equips with Continual Learning capabilities to handle evolving distributions.
  arXiv  Detail & Related papers  (2025-10-08T10:09:47Z)
• Learnable Conformal Prediction with Context-Aware Nonconformity Functions for Robotic Planning and Perception [4.694504497452662]
  Learnable Conformal Prediction replaces fixed scores with a lightweight neural function to produce context-aware uncertainty sets.<n>It maintains CP's theoretical guarantees while reducing prediction set sizes by 18% in classification, tightening detection intervals by 52%, and improving path planning safety from 72% to 91% success with minimal overhead.<n> Hardware evaluation shows LCP adds less than 1% memory and 15.9% inference overhead, yet sustains 39 FPS on detection tasks while being 7.4 times more energy-efficient than ensembles.
  arXiv  Detail & Related papers  (2025-09-26T06:44:58Z)
• Automated Quality Assessment for LLM-Based Complex Qualitative Coding: A Confidence-Diversity Framework [0.23872611575805827]
  We develop a dual-signal quality assessment framework that combines model confidence with inter-model consensus (external entropy)<n>We evaluate it across legal reasoning, political analysis, and medical classification transcripts.<n>The framework offers a principled, domain-agnostic quality assurance mechanism that scales qualitative coding without extensive double-coding.
  arXiv  Detail & Related papers  (2025-08-28T06:25:07Z)
• FS-IQA: Certified Feature Smoothing for Robust Image Quality Assessment [4.135467749401761]
  We propose a novel certified defense method for Image Quality Assessment (IQA) models.<n>It is based on randomized smoothing with noise applied in the feature space rather than the input space.<n>Our results demonstrate consistent improvements in correlation with subjective quality scores by up to 30.9%.
  arXiv  Detail & Related papers  (2025-08-07T15:47:55Z)
• Efficient Leaf Disease Classification and Segmentation using Midpoint Normalization Technique and Attention Mechanism [0.0]
  We introduce a transformative two-stage methodology, Mid Point Normalization (MPN) for intelligent image preprocessing.<n>Our classification pipeline achieves 93% accuracy while maintaining exceptional class-wise balance.<n>For segmentation tasks, we seamlessly integrate identical attention blocks within U-Net architecture using MPN-enhanced inputs.
  arXiv  Detail & Related papers  (2025-05-27T15:14:04Z)
• Beyond Confidence: Adaptive Abstention in Dual-Threshold Conformal Prediction for Autonomous System Perception [0.4124847249415279]
  Safety-critical perception systems require reliable uncertainty quantification and principled abstention mechanisms to maintain safety.<n>We present a novel dual-threshold conformalization framework that provides statistically-guaranteed uncertainty estimates while enabling selective prediction in high-risk scenarios.
  arXiv  Detail & Related papers  (2025-02-11T04:45:31Z)
• Adaptive Client Selection in Federated Learning: A Network Anomaly Detection Use Case [0.30723404270319693]
  This paper introduces a client selection framework for Federated Learning (FL) that incorporates differential privacy and fault tolerance.<n>Results demonstrate up to a 7% improvement in accuracy and a 25% reduction in training time compared to the FedL2P approach.
  arXiv  Detail & Related papers  (2025-01-25T02:50:46Z)
• Probabilistic Consensus through Ensemble Validation: A Framework for LLM Reliability [0.0]
  Large Language Models (LLMs) have shown significant advances in text generation but often lack the reliability needed for autonomous deployment. We introduce a novel framework that repurposes ensemble methods for content validation through model consensus. In tests across 78 complex cases requiring factual accuracy and causal consistency, our framework improved precision from 73.1% to 93.9%.
  arXiv  Detail & Related papers  (2024-11-10T17:32:16Z)
• How Quality Affects Deep Neural Networks in Fine-Grained Image Classification [0.799543372823325]
  We propose a No-Reference Image Quality Assessment (NRIQA) guided cut-off point selection (CPS) strategy to enhance the performance of a fine-grained classification system. We take the three most commonly adopted image augmentation configurations -- cropping, rotating, and blurring -- as the entry point. Concretely, the cut-off points yielded by those methods are aggregated via majority voting to inform the process of image subset selection.
  arXiv  Detail & Related papers  (2024-05-09T12:59:11Z)
• TernaryVote: Differentially Private, Communication Efficient, and Byzantine Resilient Distributed Optimization on Heterogeneous Data [50.797729676285876]
  We propose TernaryVote, which combines a ternary compressor and the majority vote mechanism to realize differential privacy, gradient compression, and Byzantine resilience simultaneously. We theoretically quantify the privacy guarantee through the lens of the emerging f-differential privacy (DP) and the Byzantine resilience of the proposed algorithm.
  arXiv  Detail & Related papers  (2024-02-16T16:41:14Z)
• Adaptive Feature Selection for No-Reference Image Quality Assessment by Mitigating Semantic Noise Sensitivity [55.399230250413986]
  We propose a Quality-Aware Feature Matching IQA Metric (QFM-IQM) to remove harmful semantic noise features from the upstream task. Our approach achieves superior performance to the state-of-the-art NR-IQA methods on eight standard IQA datasets.
  arXiv  Detail & Related papers  (2023-12-11T06:50:27Z)
• Breaking the Communication-Privacy-Accuracy Tradeoff with $f$-Differential Privacy [51.11280118806893]
  We consider a federated data analytics problem in which a server coordinates the collaborative data analysis of multiple users with privacy concerns and limited communication capability. We study the local differential privacy guarantees of discrete-valued mechanisms with finite output space through the lens of $f$-differential privacy (DP) More specifically, we advance the existing literature by deriving tight $f$-DP guarantees for a variety of discrete-valued mechanisms.
  arXiv  Detail & Related papers  (2023-02-19T16:58:53Z)
• (Certified!!) Adversarial Robustness for Free! [116.6052628829344]
  We certify 71% accuracy on ImageNet under adversarial perturbations constrained to be within a 2-norm of 0.5. We obtain these results using only pretrained diffusion models and image classifiers, without requiring any fine tuning or retraining of model parameters.
  arXiv  Detail & Related papers  (2022-06-21T17:27:27Z)
• Robust Semi-supervised Federated Learning for Images Automatic Recognition in Internet of Drones [57.468730437381076]
  We present a Semi-supervised Federated Learning (SSFL) framework for privacy-preserving UAV image recognition. There are significant differences in the number, features, and distribution of local data collected by UAVs using different camera modules. We propose an aggregation rule based on the frequency of the client's participation in training, namely the FedFreq aggregation rule.
  arXiv  Detail & Related papers  (2022-01-03T16:49:33Z)
• Reduced Reference Perceptual Quality Model and Application to Rate Control for 3D Point Cloud Compression [61.110938359555895]
  In rate-distortion optimization, the encoder settings are determined by maximizing a reconstruction quality measure subject to a constraint on the bit rate. We propose a linear perceptual quality model whose variables are the V-PCC geometry and color quantization parameters. Subjective quality tests with 400 compressed 3D point clouds show that the proposed model correlates well with the mean opinion score. We show that for the same target bit rate, ratedistortion optimization based on the proposed model offers higher perceptual quality than rate-distortion optimization based on exhaustive search with a point-to-point objective quality metric.
  arXiv  Detail & Related papers  (2020-11-25T12:42:02Z)
• Inducing Predictive Uncertainty Estimation for Face Recognition [102.58180557181643]
  We propose a method for generating image quality training data automatically from'mated-pairs' of face images. We use the generated data to train a lightweight Predictive Confidence Network, termed as PCNet, for estimating the confidence score of a face image.
  arXiv  Detail & Related papers  (2020-09-01T17:52:00Z)
• Towards a Competitive End-to-End Speech Recognition for CHiME-6 Dinner Party Transcription [73.66530509749305]
  In this paper, we argue that, even in difficult cases, some end-to-end approaches show performance close to the hybrid baseline. We experimentally compare and analyze CTC-Attention versus RNN-Transducer approaches along with RNN versus Transformer architectures. Our best end-to-end model based on RNN-Transducer, together with improved beam search, reaches quality by only 3.8% WER abs. worse than the LF-MMI TDNN-F CHiME-6 Challenge baseline.
  arXiv  Detail & Related papers  (2020-04-22T19:08:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.