FedHFT: Efficient Federated Fine-tuning with Heterogeneous Edge Clients

In recent years, edge servers are expected to provide not only computing resources but also caching resources for the purpose of enabling direct content delivery and offloading the data traffic from backhaul network. To overcome the restriction of limited caching resources in practice, diverse devices can be used to cache contents, which form a heterogeneous mobile edge caching network. In this paper, we study a spatial correlated caching model for heterogeneous edge caching network consisting of two kinds of caching devices: edge server and helper. A content placement strategy based on modified hard-core point process is proposed for caching helpers to provide efficient caching resources while reducing the redundancy of caching contents. The performance of the proposed strategy is analyzed using tool of stochastic geometry, in terms of hit probability, considering the effect of helper density and exclusion radius of edge servers. As validated by extensive numerical results, we are able to derive the upper bound and lower bound of hit probability and find the optimal exclusion radius of edge server to achieve a relatively high hit probability with fewer caching helpers.

With the development of network technology and the surge in demand, the speed and throughput of data and applications are leading to the skyrocketing increase in traffic. The communication and collaboration between heterogeneous edge servers are indispensable. In this scenario with heterogeneous edges, there is a common understanding on the fact that an effective edge caching algorithm could play the role of enabler to reduce the network resource consumption and content fetch delay. However, most of the existing studies on multi-agent caching methods focus more on the overall situation, while ignoring the mutual influence between different agents. In this context, we model the edge caching content replacement problem as a Markov process and deploy attention mechanism based on the Actor-Attention-Critic algorithm to realize a neighboring-aware edge caching (NAEC) strategy. The proposed method makes full use of the communication between base stations to exchange neighboring information, so that we can reduce the pressure on the backbone and further improve user satisfaction. The simulation results have verified the feasibility and effectiveness of the proposed algorithm.

In the era emphasizing the privacy of personal data, verifiable federated learning has garnered significant attention as a machine learning approach to safeguard user privacy while simultaneously validating aggregated result. However, there are some unresolved issues when deploying verifiable federated learning in edge computing. Due to the constraint resources, edge computing demands cost saving measurements in model training such as model pruning. Unfortunately, there is currently no protocol capable of enabling users to verify pruning results. Therefore, in this paper, we introduce PrVFL, a verifiable federated learning framework that supports model pruning verification and heterogeneous edge computing. In this scheme, we innovatively utilize zero-knowledge range proof protocol to achieve pruning result verification. Additionally, we first propose a heterogeneous delayed verification scheme supporting the validation of aggregated result for pruned heterogeneous edge models. Addressing the prevalent scenario of performance-heterogeneous edge clients, our scheme empowers each edge user to autonomously choose the desired pruning ratio for each training round based on their specific performance. By employing a global residual model, we ensure that every parameter has an opportunity for training. The extensive experimental results demonstrate the practical performance of our proposed scheme.

Due to the diversity of edge devices (EDs) and applications, edge systems are heterogeneous and have been applied in artificial intelligence fields, such as smart factories and intelligent transportation, which is called heterogeneous edge intelligence. Many studies employ computation offloading to transfer processing data from resource-scarce EDs to resource-rich edge servers. These studies primarily focus on the overall resource consumption of homogeneous edge systems, neglecting the system heterogeneity and the details of resource consumption. In this article, we construct a system model from a parallel perspective for the heterogeneous edge system with different processors, memory, and applications, which perceives the cost of energy and delay from three levels: system, application, and component. A hybrid metaheuristic algorithm combined with a greedy rule, hybrid mutation, and whale optimization algorithm (GHMWOA) is proposed to realize partial computation offloading. A partial offloading architecture of heterogeneous edge intelligence is proposed to validate our model and algorithm with real-world hardware and software. Experiment results not only show GHMWOA outperforms multiple classical optimization algorithms in minimizing energy consumption, but also discover on which system component energy consumption depends, and how properties of application and system influence the cost of energy.

Edge computing enables smart IoT-based systems via concurrent and continuous execution of latency-sensitive machine learning (ML) applications. These edge-based machine learning systems are often battery-powered (i.e., energy-limited). They use heterogeneous resources with diverse computing performance (e.g., CPU, GPU, and/or FPGA) to fulfill the latency constraints of ML applications. The challenge is to allocate user requests for different ML applications on the Heterogeneous Edge Computing Systems (HEC) with respect to both the energy and latency constraints of these systems. To this end, we study and analyze resource allocation solutions that can increase the on-time task completion rate while considering the energy constraint. Importantly, we investigate edge-friendly (lightweight) multi-objective mapping heuristics that do not become biased toward a particular application type to achieve the objectives; instead, the heuristics consider "fairness" across the concurrent ML applications in their mapping decisions. Performance evaluations demonstrate that the proposed heuristic outperforms widely-used heuristics in heterogeneous systems in terms of the latency and energy objectives, particularly, at low to moderate request arrival rates. We observed 8.9% improvement in on-time task completion rate and 12.6% in energy-saving without imposing any significant overhead on the edge system.

Multiwavelength light transmission imaging provides a possibility for early detection of breast cancer. However, due to strong scattering during the transmission process of breast tissue analysis, the transmitted image signal is weak and the image is blurred and this makes heterogeneous edge detection difficult. This paper proposes a method based on the weighted constraint decision (WCD) method to eliminate the erosion and checkerboard effects in image histogram equalization (HE) enhancement and to improve the recognition of heterogeneous edge. Multiwavelength transmission images of phantom are acquired on the designed experimental system and the mask image with high signal-to-noise ratio (SNR) is obtained by frame accumulation and an Otsu thresholding model. Then, during image enhancement the image is divided into low-gray-level (LGL) and high-gray-level (HGL) regions according to the distribution of light intensity in image. And the probability density distribution of gray level in the LGL and HGL regions are redefined respectively according to the WCD method. Finally, the reconstructed image is obtained based on the modified HE. The experimental results show that compared with traditional image enhancement methods, the WCD method proposed in this paper can greatly improve the contrast between heterogeneous region and normal region. Moreover, the correlation between the original image data is maintained to the greatest extent, so that the edge of the heterogeneity can be detected more accurately. In conclusion, the WCD method not only accurately identifies the edge of heterogeneity in multiwavelength transmission images, but it also could improve the clinical application of multiwavelength transmission images in the early detection of breast cancer.

In this study, four species of EntolomasubgenusCyanula (E.orientosinense, E.subgriseosquamulosum, E.subpraegracile, and E.wuyishanense) from subtropical regions of China, are described as new to science based on morphological and phylogenetic analyses. Morphologically, E.orientosinense is characterized by the white basidiomata, relatively large basidiospores, and carneogriseum-type lamellae edge; E.subgriseosquamulosum is recognized by the fuscous pileus, crowded and adnate lamellae, and medium-sized basidiospores; E.subpraegracile is identified by the yellow pileus and intervenose lamellae with sterile or heterogeneous edge; E.wuyishanense is distinct by the blue basidiomata and fertile lamellae edge with slightly bluish pigmentation near the stipe. Entolomaorientosinense belongs to sect. Caesiocincta, subsect. Queletia, and E.wuyishanense belongs to sect. Poliopodes. The remaining two species each form independent branches and do not belong to any known sections. Detailed descriptions, color photos, and a key to related species are presented.

The patch-based polarimetric synthetic aperture radar (PolSAR) nonlocal means (NLM) speckle filters are efficacious in noise suppression and detail preservation, but are computationally inefficient. The objective of this paper is to develop a filter that provides better noise suppression and edge preservation along with reduced computational complexity for PolSAR applications. In this paper, the patch-based NLM adaptive speckle filter based on constant false alarm rate (CFAR) edge detector is proposed. The CFAR-based edge detector is used to generate a map that classifies the data into three regions: homogeneous, heterogeneous, and strong edge dominant regions. The proposed speckle filter adapts itself suitably based on the heterogeneity of the region using classified regions as a mask. The performance of proposed filtering technique is evaluated on 1-look simulated (generated by Monte Carlo simulation), 1-look RADARSAT-2, and 4-look AIRSAR data. The performance evaluation is done based on the extent of noise reduction measured by equivalent number of looks, edge preservation degree, bias in estimation, polarimetric structure preservation, and visual appearance. The performance of the proposed filtering technique is found to be better than the state-of-the-art speckle filtering techniques like refined Lee, NLM pretest, and NL-SAR. The order of computational complexity of the proposed filter is found to be better than the pretest or NL-SAR filters.

Convolutional neural network (CNN)-based object detection is a key technology to enable autonomous mobile vision applications on mobile end devices such as smart phones and drones. With the advance of edge computing technology, a prevalent solution is to offload the computation-intensive CNN inference tasks to edge networks for fast and accurate object detection. However, a single edge server may not be powerful enough to ensure fast and accurate object detection due to its resource constraints. In this paper, we propose a multi-edge assisted fast object detection framework, MASS, to further reduce the object detection latency while maintaining the detection accuracy. In MASS, the CNN model is divided into two parts, namely the Head part and the Tail part. The Head part is executed locally and the Tail part is further split into multiple subtasks, which can be offloaded to multiple edge servers for parallel execution. First, we propose a method to select the optimal parallel entry point to separate the Head part from the Tail part. Then, an adaptive subtask generation and offloading strategy is proposed to divide the Tail part into multiple subtasks and offload these subtasks to multiple heterogeneous edge servers. Besides, we also propose a uniformly sampled zero-padding scheme to reduce the communication cost among edge servers when executing these subtasks in parallel. We implement MASS in a testbed with four edge servers and evaluate its performance, where the experimental results show that MASS can reduce object detection latency by up to $64.83%$, while the detection accuracy degradation is less than $3%$.

Parallelizing CNN inference on heterogeneous edge clusters with data parallelism has gained popularity as a way to meet real-time requirements without sacrificing model accuracy. However, existing algorithms struggle to find optimal parallel granularity for complex CNNS, the structure of which is a directed acyclic graph (DAG) rather than a chain, and the parallel dimension is inflexible. To distribute the workload of modern CNNs on heterogeneous devices is also proven as NP-hard problem. In this article, we introduce DeepZoning , a versatile and cooperative inference framework that combines both model and data parallelism to accelerate CNN inference. DeepZoning employs two algorithms at different levels: (1) a low-level Adaptive Workload Partition algorithm that uses linear programming and takes spatial and channel dimensions into optimization during the search for feature map distribution on heterogeneous devices, and (2) a high-level Model Partition algorithm that finds the optimal model granularity and organizes complex CNNs into sequential zones to balance communication and computation during execution. Our experimental evaluations show that DeepZoning is effective, achieving up to a 3.02× speed improvement on our experimental prototype compared to state-of-the-art algorithms.

Federated robotic task execution systems require bridging natural language instructions to distributed robot control while efficiently managing computational resources across heterogeneous edge devices without centralized coordination. Existing approaches face three limitations: rigid hand-coded planners requiring extensive domain engineering, centralized coordination that contradicts federated collaboration as robots scale, and static resource allocation failing to share containers across robots when workloads shift dynamically. We present SwiftBot, a federated task execution platform that integrates LLM-based task decomposition with intelligent container orchestration over a DHT overlay, enabling robots to collaboratively execute tasks without centralized control. SwiftBot achieves 94.3% decomposition accuracy across diverse tasks, reduces task startup latency by 1.5-5.4x and average training latency by 1.4-2.5x, and improves tail latency by 1.2-4.7x under high load through federated warm container migration. Evaluation on multimedia tasks validates that co-designing semantic understanding and federated resource management enables both flexibility and efficiency for robotic task control.

Mobile Edge Computing (MEC) leverages computational heterogeneity between mobile devices and edge nodes to enable real-time applications requiring high information fresh ness. The Age-of-Information (AoI) metric serves as a crucial evaluator of information timeliness in such systems. Addressing AoI minimization in multi-user MEC environments presents significant challenges due to stochastic computing times. In this paper, we consider multiple users offloading tasks to heterogeneous edge servers in an MEC system, focusing on preemptive and non-preemptive task scheduling mechanisms. The problem is first reformulated as a Restless Multi-Arm Bandit (RMAB) problem, with a multi-layer Markov Decision Process (MDP) framework established to characterize AoI dynamics in the MEC system. Based on the multi-layer MDP, we propose a nested index framework and design a nested index policy with provably asymptotic optimality. This establishes a theoretical framework adaptable to various scheduling mechanisms, achieving efficient optimization through state stratification and index design in both preemptive and non-preemptive modes. Finally, the closed-form of the nested index is derived, facilitating performance trade-offs between computational complexity and accuracy while ensuring the universal applicability of the nested index policy across both scheduling modes. The experimental results show that in non preemptive scheduling, compared with the benchmark method, the optimality gap is reduced by 25.43%, while in preemptive scheduling, the gap has reduced by 21.21%. As the system scale increases, it asymptotically converges in two scheduling modes and especially provides near-optimal performance in a non preemptive structure.

PoVIChain: Lightweight and verifiable cross-chain interoperability for heterogeneous edge networks

Mobile edge computing (MEC) is envisioned as a promising solution to real-time services in Internet of Vehicles (IoV) by enabling edge caching, computing and communication. However, it is still challenging to implement multimedia streaming in MEC-based IoV due to dynamic vehicular environments and heterogeneous network resources. In this paper, we present an MEC-based architecture for adaptive-bitrate-based (ABR) multimedia streaming in IoV, where each multimedia file is segmented into multiple chunks encoded with different bitrate levels. Then, we formulate a joint resource optimization (JRO) problem by synthesizing heterogeneous edge cache and communication resource constraints, which aims at achieving both smooth play and high-quality service by optimizing chunk placement and transmission. For chunk placement, a multi-armed bandit (MAB) algorithm is proposed for online scheduling with low overhead but slow convergence. Further, a deep-Q-learning algorithm is proposed to improve cache reward and speed up convergence by using replay memory for repeatedly training. For chunk transmission, we design an adaptive-quality-based chunk selection (AQCS) algorithm, which determines bandwidth allocation and quality level based on a benefit function incorporating quality level, available playback time, and freezing delay. Lastly, we build the simulation model and give comprehensive performance evaluation, which demonstrates the superiority of proposed algorithms.

Tasks allocation at the edge of the network is a significant research topic for the upcoming new era of the intelligent edge mesh. One can easily detect interesting attempts to define novel algorithms for distributing tasks into a number of heterogeneous edge nodes. Nodes interact in very dynamic environments, thus, their availability/capability of efficiently executing tasks in real time varies. In this paper, we propose a model for allocating tasks under the uncertainty present in an edge computing environment. The uncertainty is related to the status of edge nodes and their availability for performing the requested processing activities. To manage this uncertainty, we adopt a Type-2 Fuzzy Logic system and propose a novel approach for delivering the appropriate fuzzy sets for input and output variables. Our methodology is fully adapted to nodes’ status as exposed by statistical reports exchanged at pre-defined intervals. We propose a data-driven approach that delivers the upper and lower bounds of our Type-2 fuzzy sets and present the corresponding model. We incorporate the uncertainty management mechanism into the decision making model of edge nodes being responsible to select the most appropriate peers for offloading tasks that are not possible to be executed locally. We present the performance of the proposed model through an extensive experimental evaluation and reveal its pros and cons.