Xinyue Zhang

Energy efficiency is essential for federated learning (FL) over mobile devices and its potential prosperous applications. Different from existing communication efficient FL research efforts, which regard communication energy consumption as the bottleneck, we have observed that with ever increasing wireless transmission speed (e.g., Wi-Fi 5 or 5G), the energy consumption of wireless communications for model updates in FL is significantly reduced and sometimes is smaller than that of local on-device training. Motivated by such observations, in this paper, we propose a high-speed wireless communications inspired energy efficient federated learning over mobile devices (EEFL), whose goal is to reduce the overall energy consumption (computing + communication). In particular, we design a novel energy-aware adaptive local update policy for mobile devices by jointly considering FL performance and energy saving of high-speed wireless transmissions. Furthermore, given the device’s local update policy in each FL global round, we advance the dynamic voltage and frequency scaling (DVFS) strategy to minimize local training’s energy consumption by keeping GPU and CPU working at appropriate frequencies without triggering thermal throttling. Extensive experimental results with various learning models, datasets, and wireless transmission environments demonstrate the proposed EEFL’s superiority over the peer designs in terms of energy efficiency.

The accurate segmentation of pigmented skin lesions is a critical prerequisite for reliable melanoma detection, yet approximately 30% of lesions exhibit fuzzy or poorly defined borders. This ambiguity makes the definition of a single contour unreliable and limits the effectiveness of computer-assisted diagnosis (CAD) systems. While clinical assessment based on the ABCDE criteria (asymmetry, border, color, diameter, and evolution), dermoscopic imaging, and scoring systems remains the standard, these methods are inherently subjective and vary with clinician experience. We address this challenge by reframing segmentation into three distinct regions: background, border, and lesion core. These regions are delineated using superpixels generated via the Simple Linear Iterative Clustering (SLIC) algorithm, which provides meaningful structural units for analysis. Our contributions are fourfold: (1) redefining lesion borders as regions, rather than sharp lines; (2) generating superpixel-level embeddings with a transformer-based autoencoder; (3) incorporating these embeddings as features for superpixel classification; and (4) integrating neighborhood information to construct enhanced feature vectors. Unlike pixel-level algorithms that often overlook boundary context, our pipeline fuses global class information with local spatial relationships, significantly improving precision and recall in challenging border regions. An evaluation on the HAM10000 melanoma dataset demonstrates that our superpixel–RAG–transformer (region adjacency graph) pipeline achieves exceptional performance (100% F1 score, accuracy, and precision) in classifying background, border, and lesion core superpixels. By transforming raw dermoscopic images into region-based structured representations, the proposed method generates more informative inputs for downstream deep learning models. This strategy not only advances melanoma analysis but also provides a generalizable framework for other medical image segmentation and classification tasks.

Melanoma remains one of the most lethal forms of skin cancer. In clinical practices, primary care physicians typically rely on the ABCDE criteria (Asymmetry, Border, Color, Diameter, and Evolution), alongside dermoscopic examination and scoring systems, to assess lesion malignancy. However, these assessments are inherently subjective and often influenced by the clinician’s level of experience. To address this limitation, Computer-Assisted Diagnosis (CAD) systems have been developed to provide more objective and reproducible evaluations. CAD algorithms either extract ABCD-related features or directly classify lesions from dermoscopic images. In both approaches, accurate lesion segmentation is critical. Yet, approximately 30% of skin lesions exhibit fuzzy or poorly defined borders, complicating the task of drawing a single, definitive contour. In this work, we identify three distinct classes in dermoscopic images (background, border, and lesion core) based on superpixels generated via the Simple Linear Iterative Clustering (SLIC) algorithm. Our contributions are fourfold: (1) redefining lesion borders as regions rather than lines; (2) generating superpixel-level embeddings using a transformer-based autoencoder; (3) incorporating these embeddings as features for classification; and (4) integrating neighborhood information to construct enriched feature vectors. Unlike pixel-level CNN algorithms that often overlook fine-grained boundary contexts, our pipeline fuses global class context with local spatial relationships, significantly improving precision and recall in challenging border regions. Extensive evaluation on the HAM10000 melanoma dataset demonstrates that our superpixel-RAG-transformer pipeline achieves exceptional performance in classifying background, border, and lesion core superpixels.