Federated learning in mobile edge systems faces a key challenge: how to train models efficiently while still allowing slow devices to participate. Many existing methods either drop slow devices to speed up training, which hurts fairness, or wait for all devices, which wastes the computing power and time of faster ones. CLAMP addressed this through dynamic depth selection, allowing each client to train an adjusted number of model layers based on their capabilities. However, CLAMP's minimum depth aggregation creates a bottleneck. The server only aggregates the layers trained by all clients, which means that updates from deeper layers trained by faster devices are ignored. This strategy leaves the model capacity unused and limits the convergence speed. We propose Mask-Aware CLAMP, which replaces minimum-depth aggregation with layer-wise mask-aware aggregation. MA-CLAMP aggregates each model layer independently using only the clients that trained it, subject to minimum participation thresholds that ensure stability. This approach preserves deeper layer updates from capable clients while maintaining CLAMP's adaptive depth selection and straggler handling. MA-CLAMP does not require changes to client-side training. Experiments across MNIST, Fashion-MNIST, and CIFAR-10 with 100 heterogeneous clients show that MA-CLAMP achieves faster convergence on moderately complex tasks, improved resource efficiency in computation and energy use, maintains or improves model accuracy across all datasets, and enhances slow-device inclusion rates. MA-CLAMP provides a more effective solution for production federated learning systems where model quality, resource efficiency, and fair client participation are all essential.
Since the invention of automobiles, engineers have spent endless efforts to enhance their safety, operability, and fuel efficiency. The rapid development of electric vehicles (EVs) has transformed them into movable computing hubs, equipped with ample sensing, computing capabilities, and battery power. However, the placement of all the vehicle's sensors within its structure poses limitations to its sensing capability, consequently restricting potential applications. Meanwhile, unmanned aerial vehicles (UAVs or drones) offer aerial-perspective sensing capability from flexible viewpoints, processing which in real time can complement cars' sensing and enable novel applications. In this paper, we introduce our vision of how the real-time collaboration of ground and aerial vehicles can enable innovative mobility solutions. We discuss the system architectures, potential usage scenarios, and enabling technologies for supporting drone-car collaboration. Furthermore, we examine the latest technical advancements in the field and shed light on the promising opportunities that lie ahead in the future. Overall, this article provides a comprehensive understanding of the potential and significance of real-time collaboration between drones and cars in revolutionizing future mobility, improving fuel efficiency, and enhancing driving safety.
To provide timely outdoor emergency rescue, it is necessary to assess the human injury severity (HIS) in real time and proactively send a distress signal when an injury is detected. In the past years, there have been many studies on HIS assessment. However, these studies commonly use bulky medical equipment for human physiological measurements, making them inappropriate to be applied outdoors. Given that Photoplethysmogram (PPG) sensors, which are small in size and low in power cost, can measure a series of human physiological indicators, many studies have embedded PPG sensors into wearable devices, e.g., smartwatches, to enable human health monitoring. Based on PPG technology, a series of human physiological indicators such as heart rate, respiratory rate, oxygen saturation and blood pressure can be measured over long periods of time. This article aims to investigate the way of using a wearable PPG device to assess HIS for outdoor activity scenarios. First, the feasibility of using PPG-derived data to assess HIS is discussed. Then, a database used for HIS assessment research is built. Next, the PPG-based human physiological features used for HIS assessment are extracted. Later, six HIS assessment models, including LR, LDA, Linear_SVM, RBF_SVM, RF and CatBoost, were investigated. Experimental results show that the CatBoost model demonstrates overall optimal performance for assessing mild injury cases, while the RBF_SVM model exhibits superior performance for assessing moderate and severe injury cases.
Data spaces provide governed ecosystems for sovereign data exchange among organizations. However, current implementations rely on static, accept-or-reject contract mechanisms that limit agreement flexibility and constrain ecosystem adoption through suboptimal or failed negotiations. This work presents an automated and bilateral negotiation framework for multi-issue contracts in data spaces, aligned with the Dataspace Protocol (DSP) and the Open Digital Rights Language (ODRL) standards. The framework comprises a negotiation engine that implements iterative, time-dependent concession strategies, utility-based evaluation, and opponent modeling, together with a translator module that provides bidirectional mapping between ODRL policy constructs and the negotiation logic. The framework design is grounded in a practitioner needs assessment that identifies current negotiation gaps, confirms demand for automated support, and informs the selection of negotiation issues and the human-in-the-loop approval mechanism. The framework is integrated with the Eclipse Dataspace Components (EDC) and validated in an urban data-sharing scenario. A systematic evaluation compares the framework against baseline strategies, analyzes the effect of concession pace on the efficiency-welfare tradeoff, and characterizes framework robustness across varying degrees of preference overlap between parties.
Deciding which colors to use for a grayscale film without any guidance can lead to a myriad of colorization outputs, with some more believable than others. Unlike cinema, the lighter burden of colorizing photos allows human text guidance to be used. This paper attempts to take advantage of recent advances in large language models and neuro-symbolic artificial intelligence (AI) to extend human-guided image colorization to the video domain. The instantiation of this methodology we call " RAGCol++ : R etreival A ugemented G eneration based automatic video Col orization using Semantic Similarity Search and Probabilistic Grounded Knowledge". This work improves upon the original RAGCol paper by expanding the size and quality of COL-KG, adding efficiencies to the automatic video colorizer and incorporating semantic similarity search as opposed to the original Cypher query-based search. Our system achieves an average improvement of 3% over the previous state-of-the-art L-CAD + BVD across the DAVIS and Videvo datasets when using PSNR, SSIM, FID, FVD, and CDC as the metrics. This result is also substantiated by our user study, where RAGCol++ was preferred 56% of the time.
