Collaborative learning between edge servers (e.g., base stations) and end devices (e.g., drones) enables simultaneous model training in web applications through knowledge sharing. The resulting models effectively reduce service latency. However, existing approaches either assume isomorphic models on edge servers and end devices or incur substantial transmission overhead when training. Moreover, edge servers are often unable to access data from end devices on time due to long-distance constraints or strict data privacy regulations. This paper proposes a Prototype Augmentation-based Edge-end Collaborative Learning method (PAECL). It simultaneously trains heterogeneous edge and end models in the absence of data on edge servers by transmitting only augmented class-wise feature vectors (prototypes), significantly reducing communication overhead compared to sharing models, data, or logits. Specifically, on end devices, prototype-implied latent knowledge is augmented via local prototype contrast and global prototype alignment. On edge servers, prototypes are further augmented to produce bounded virtual vectors by mixing them with random noise, and the augmented prototypes are then delivered to generative models to provide data during edge model training. Through simulations and field experiments, PAECL achieves the highest accuracy for edge and end models under limited training resources and reduces the transmission burden by at least 297 times compared to existing edge-end heterogeneous learning methods.

This paper addresses theJoint deployment ofUnmanned aerial vehicles (UAVs) and surveillance caMeras withPath planning (JUMP), aiming to deploy a fixed number of UAVs and budget-limited surveillance cameras, to achieve continuous omnidirectional monitoring. Specifically, the objective is to maximize the monitoring durations of target objects in each of all horizontal directions within a given task duration. We propose an approach for JUMP, which is proved to be NP-hard. Our approach achieves a$\frac{1}{6}-\varepsilon _{1}$approximation ratio in general and$\frac{1}{4}-\varepsilon _{1}$when camera costs are uniform. Firstly, we introduce spatio-temporal discretization to approximate JUMP. Secondly, we partition the solution space of JUMP from spatial perspective and refine the space by addressing a variant of obstacle-avoiding shortest path problem spatio-temporal perspective. Thirdly, we reformulate the problem as a classical problem of Monotone Submodular set function Maximization with one partition Matroid and two Knapsack constraints (MSMMK). To address MSMMK, we propose a$\frac{1}{6(1+\varepsilon )}$approximation algorithm, which outperforms the state-of-the-art with the same time complexity in general. Specifically, our algorithm achieves a$\frac{1}{4(1+\varepsilon )}$approximation ratio for special cases. Simulation results demonstrate our proposed approach outperforms five benchmark algorithms, yielding enhancements 13%-1446%. Moreover, field experiment results indicate that our approach surpasses comparison algorithms, achieving enhancements 23%-265%.

Edge-end collaborative learning trains models with exchanged knowledge through distributed interaction, alleviating the cloud's burden. Edge-end heterogeneous collaborative learning further enables edge servers and end devices to train models of different scales in parallel based on their computational capabilities. This technology supports various applications in different resource conditions and improves server resource utilization. However, implementing it is challenging due to heavy communication costs and high global costs (time and energy). To this end, this paper proposes a novel prototype-based edge-end heterogeneous collaborative learning method and an optimization algorithm, which improves model performance and reduces training costs. We first use prototypes to perform collaborative learning and analyze the convergence. Prototypes are computed as mean feature vectors from different classes. The aggregated prototypes help capture class information on end devices and generate data on edge servers. Then, we study how to determine prototype selection and edge association to minimize training time, energy consumption, and prototype approximation error under a limited reward budget, which is proven to be NP-hard. We split the original problem into two subproblems. The first is solved in the closed form. Through approximation and reformulation, the second is transformed into a submodular maximization problem with knapsack and matroid constraints. On this basis, we propose an approximation algorithm with a theoretical guarantee. Finally, by simulation and field experiments, our method takes 3.61% of communication costs to improve heterogeneous edge and end models' accuracy by at least 5.16% and 2.77% compared with five baselines. The proposed algorithm outperforms others by at least 9.78% in terms of global cost.

The basal diameter of uveal melanoma is critical for its prognosis and therapy, and it can indicate the metastatic risk of the tumor. Tumor segmentation is also significant for guiding clinical diagnosis, as it provides morphological and other essential information for clinicians. However, precise uveal melanoma segmentation and basal diameter prediction remain challenging for current computer-aided methods. The scarcity of large, annotated datasets and appropriate multi-task models hinders further exploration to simultaneously and accurately segment the tumors and predict their basal diameter for uveal melanoma. To address this challenge, we collected a novel dataset and proposed the Tumor Segmentation and Basal Diameter Prediction Network for Uveal Melanoma (TSBPNet-UM), which utilizes readily accessible fundus images to accomplish both tasks concurrently. Its two sub-networks, the Uveal Melanoma Segmentation and Basal Diameter Prediction networks, are designed for mutual performance enhancement. The Uveal Melanoma Segmentation network transfers spatial segmentation information to assist the Basal Diameter Prediction network. Conversely, the Basal Diameter Prediction network updates all parameters using basal diameter-derived gradient information. This model effectively overcomes the limitation that conventional models often struggle to converge on the direct prediction task of basal diameter. The superior performance and efficacy of the TSBPNetUM have been demonstrated through extensive experiments.

This paper considers a canonical problem in kernel regression: how good are the model performances when it is trained by the popular online first-order algorithms, compared to the offline ones, such as ridge and ridgeless regression? In this paper, we analyze the foundational single-pass Stochastic Gradient Descent (SGD) in kernel regression under source condition where the optimal predictor can even not belong to the RKHS, i.e. the model is misspecified. Specifically, we focus on the inner product kernel over the sphere and characterize the exact orders of the excess risk curves under different scales of sample sizes $n$ concerning the input dimension $d$. Surprisingly, we show that SGD achieves min-max optimal rates up to constants among all the scales, $without$ suffering the saturation, a prevalent phenomenon observed in (ridge) regression, except when the model is highly misspecified and the learning is in a final stage where $n\gg d^\gamma$ with any constant $\gamma >0$. The main reason for SGD to overcome the curse of saturation is the exponentially decaying step size schedule, a common practice in deep neural network training. As a byproduct, we provide the $first$ provable advantage of the scheme over the iterative averaging method in the common setting.

Edge-end collaborative learning greatly reduces latency by eliminating the need for processing on the cloud side, showing promising results in machine learning applications due to collaborating on training tasks through the computational resources of edge servers and end devices. However, current edge-end collaborative learning methods suffer from unbearable latency which result from heavy transmission burden and long synchronization time. We propose a new Prototype-based Semi-Asynchronous Edge-end Collaborative Learning approach (ProSACL) that carefully integrates prototype-based end-side training and edge clustering, allowing any end device to synchronize knowledge, significantly reducing the time spent on training latency. Our approach includes (1) prototype training and asynchronous prototype transfer on end devices: Unlike traditional training methods, we use the average of the same class of feature vectors, i.e., the prototype, as the knowledge transfer means, which significantly reduces the transfer burden and eliminates the need for end devices to wait for other devices to finish. (2) prototype-based clustering and asynchronous aggregation on the edge server: The end devices are segmented by prototype-based clustering to obtain unbiased prototypes for performance enhancement, and prototypes from different rounds are aggregated for fine-grained knowledge transfer. We evaluate the proposed approach by training on three datasets, which show substantial performance improvement compared to previous work.

ABSTRACT Context The rise of artificial intelligence of things (AloT) has enabled smart cities and industries, and UAV‐assisted edge computing networks are an important technology to support the above scenarios. UAV‐assisted refers to leveraging UAVs as a dynamic, flexible infrastructure to assist edge network data processing and communication tasks. Multiple UAVs can use their own resources, and collaborate edge servers to train artificial intelligence (Al) models. Objective Compared with cloud‐based collaborative computing scenarios, UAV‐assisted edge collaborative learning can reduce training and inference delays and improve user satisfaction. However, UAV‐assisted edge networks scenario brings new challenges in terms of transmission burden and energy consumption. Method This paper proposes a prototype‐based joint optimization and training software system. The system consists of an optimization module and a training module. The optimization module first models an optimization problem including energy consumption and prototype error. Then it solves the optimization problem by problem transformation and plans the location of each UAV given the objects' position. After UAVs fly to the designated area and complete data collection, UAVs and the edge server train a model according to the proposed prototype‐based collaborative training module. Our training module enables multiple UAVs and an edge server to collaboratively train a model by lightweight prototype transmission and prototype aggregation. We also prove the convergence of the proposed collaborative training method. Results Results show our method reduces prototype error and energy consumption by at least 12.31% and improves model accuracy by 3.62% with a little communication burden. Conclusion Finally, we verify system performance through experiments.

For Unmanned Aerial Vehicles (UAVs) monitoring tasks, capturing high quality images of target objects is important for subsequent recognition. Concerning the problem, many prior works study placement/trajectory planning for UAVs to maximize the quality of captured images. However, all of them overlook a fact thatUAV monitoring may cause a huge risk/annoyance on living objects.In this paper, we investigate the novel problem of oPtimizing uncrewed aErial vehicles plAcement byConsidering both monitoring utility and adverseEffects (PEACE). We propose an approach to solve PEACE, which is proved to be NP-hard. Overall, our approach achieves a$1- \frac{1}{e}-\varepsilon$approximation ratio. First, we approximate the original problem of PEACE as a classical problem of Monotone Submodular function Maximization under a Uniform Matroid constraint (MSMUM) with a controlled gap. Then, for MSMUM, we propose a combination of algorithms achieving a$1-\frac{1}{e}$approximation and$O(n\log n)$time complexity considering the correlation among the UAV monitoring strategies. The proposed algorithms outperform existing algorithms for MSMUM through theoretical analysis and experimental results. Extensive simulations and field experiments demonstrate the effectiveness of our approach, achieving performance gains of 9.0% to 1434.5% compared to existing methods.

Large-scale urban pipeline systems (LSUPSs) are complex pipeline networks of flows (e.g., water, oil, or gas). Flow capacity intelligent monitoring, e.g., automatically monitor the sum of flows in an LSUPS, is an important task in smart cities. Recently, mobile sensors and static receiver nodes are used to perform the task. Mobile sensors are released into the network at selected entrances to collect data, and upload the data to receiver nodes deployed at selected locations of the network for further analysis. Due to cost constraints, the numbers of mobile sensors and receiver nodes are limited, which cause the problem that some pipelines may not be monitored. However, applications normally require that some Zones of Interest (ZoIs) in the network have to be monitored. Therefore, how to select optimal entrances and locations for given numbers of mobile sensors and receiver nodes, so that the capacity of monitored flow is maximized within a given time under the constraint that all ZoIs are also monitored with expected probabilities, is a challenging problem. First, we prove the problem is NP-complete. Then, we design two algorithms based on submodular set function optimization to solve it. The first algorithm can obtain an approximate optimal solution with high time complexity, while the second algorithm can obtain a suboptimal solution with much lower time complexity. Finally, we analyze time complexity and approximate ratio of the two algorithms. Theoretical analyses and simulation results show that the proposed algorithms outperform the state-of-the-art algorithms.

Large-scale acyclic flow systems (LSAFSs) are models of pipeline networks that are used to transport important resources, such as water, oil, and natural gas in smart cities. LSAFSs have features of complex topology and deep-underground deployment, which would cause accidents, such as leakages and pollution that are difficult to be detected in time. Mobile sensors (MSs)-based monitoring schemes appear as an effective solution in recent years to handle this situation. These schemes drop MSs into an LSAFS from specified locations, and the MSs will move along with fluid inside the LSAFS to collect data. When the MSs pass through a predeployed and activated receiver node (RN), they will upload their data to the RN. However, how to decide the optimal locations and timings for dropping of the MSs and the best activation periods and deployment locations of the RNs, such that total length of monitored pipelines in the LSAFS is maximized, as well as energy consumption of the RNs is minimized, is a challenging problem. The problem is more challenging if potential uploading failures and undetermined movement of the MSs are considered. In this article, we first formalize the problem as a multiobjective optimization problem. Then, we decompose the problem into a submodular optimization problem and an union set optimization problem, and prove they are NP-hard. Next, an approximate algorithm based on the Pigeonhole principle is proposed to solve the first problem, and a heuristic algorithm based on the inclusion-exclusion principle is proposed to solve the second problem. Extensive theoretical analyses and simulations show that the proposed algorithms outperform state-of-the-art algorithms.

This paper investigates the effects of real time visual biofeedback for improving sports performance using a large scale immersive mixed reality system in which users are able to play a simulated game of curling. The users slide custom curling stones across the floor onto a projected target whose size is dictated by the user's stress-related physiological measure; heart rate (HR). The higher HR the player has, the smaller the target will be, and vice-versa. In the experiment participants were asked to compete in three different conditions: baseline, with and without the proposed biofeedback. The results show that when providing a visual representation of the player's HR or "choking" in competition, it helped the player understand their condition and improve competition performance (P-value of 0.0391).