The distributed linearly separable computation problem finds extensive applications across domains such as distributed gradient coding, distributed linear transform, real-time rendering, etc. This paper investigates this problem in a decentralized network, where N workers, connected through a shared device-to-device (D2D) link, collaboratively perform the computation task without a central master. Each worker aims to compute a linearly separable function that can be manifested as Kclinear combinations of K messages, where each message is a function of a distinct dataset. The system is designed to tolerate up to N − Nrstragglers, ensuring that each worker can successfully complete the task based on transmissions from any Nrworkers. Our goal is to minimize the communication cost (the number of symbols transmitted by the fastest Nrworkers), under arbitrary computation cost (the number of uncoded datasets assigned to each worker). For the scenario where the computation cost is minimal, we propose a novel distributed computing scheme that is optimal under the widely used cyclic data assignment. Interestingly, we demonstrate that the side information at each worker is ineffective in the decoding phase when Kc≤ KNr/N, while it becomes beneficial as Kcincreases. Additionally, we extend the proposed scheme to scenarios where the computation cost is not necessarily minimum, and derive the optimal computation-communication costs tradeoff under the cyclic assignment when Kcis comparatively large.

The distributed linearly separable computation problem finds extensive applications across domains such as dis-tributed gradient coding, distributed linear transform, real-time rendering, etc. In this paper, we investigate this problem in a fully decentralized scenario, where$\mathrm{N}$workers collaboratively perform the computation task without a central master. Each worker aims to compute a linearly separable computation that can be manifested as$\mathrm{K}_{\mathrm{c}}$linear combinations of$\mathrm{K}$messages, where each message is a function of a distinct dataset. We require that each worker successfully fulfill the task based on the transmissions from any$\mathrm{N}_{\mathrm{r}}$workers, such that the system can tolerate any$\mathrm{N}-\mathrm{N}_{\mathrm{r}}$stragglers. We focus on the scenario where the computation cost (the number of uncoded datasets assigned to each worker) is minimum, and aim to minimize the communication cost (the number of symbols the fastest$\mathrm{N}_{\mathrm{r}}$workers transmit). We propose a novel distributed computing scheme that is optimal under the widely used cyclic data assignment. Interestingly, we demonstrate that the side information at each worker is ineffective in reducing the communication cost when$\mathrm{K}_{\mathrm{c}}\leq \text{KN}_{\mathrm{r}}/\mathrm{N}$, while it helps reduce the communication cost as$\mathrm{K}_{\mathrm{c}}$increases.

Cyberattacks on digital services and Internet of Things (IoT) are rising, employing complex tactics. Using intrusion detection systems (IDS) to detect and counter threats at key network points is vital for strong cybersecurity. Traditional rule-based network IDS rely on predefined rules, which may not effectively recognize the myriad complex variants of potential attacks. AI-driven methods for detecting malicious traffic offer enhanced capabilities but can fall short in terms of interpretability and performance under high-throughput network conditions. To address these challenges, we propose a LLM-based (Large Language Model) agent that utilizes multiple sources inputs to generate and generalize rules. The generated rules are designed to detect a variety of corresponding malicious threats, while the generalized rules are crafted to identify similar variant attacks. We have amassed an extensive dataset, comprising vulnerability security reports, malicious traffic, and original IDS rules from authoritative sources, which serve as input for the LLM-based agent. Subsequently, comparative experiments were conducted to assess the performance of the new rules in detecting malicious traffic. The experimental results demonstrate the superior performance of these new rules across various metrics for malicious traffic detection.

Edge computing has recently garnered significant interest in many Internet of Things (IoT) applications. However, the excessive overhead during data exchange still remains an open challenge, especially for large-scale data processing tasks. This paper considers a master-aided distributed computing system with multiple edge computing nodes and a master node, where the master node helps edge nodes compute output functions. We propose a coded scheme to reduce the communication latency by exploiting computation and communication capabilities of all nodes and creating coded multicast opportunities. More importantly, we prove that the proposed scheme is always optimal, i.e., achieving the minimum communication latency, for arbitrary computing and storage abilities at the master. This extends the previous optimality results in the extreme cases (either the master could compute all input files or compute nothing) to the general case. Finally, numerical results and TeraSort experiments demonstrate that our schemes can greatly reduce the communication latency compared with the existing schemes.

A central issue of distributed computing systems is how to optimally allocate computing and storage resources and design data shuffling strategies such that the total execution time for computing and data shuffling is minimized. This is extremely critical when the computation, storage and communication resources are limited. In this paper, we study the resource allocation and coding scheme for the MapReduce-type framework with limited resources. In particular, we focus on the coded distributed computing (CDC) approach proposed by Li et al.. We first extend the asymmetric CDC (ACDC) scheme proposed by Yu et al. to the cascade case where each output function is computed by multiple servers. Then we demonstrate that whether CDC or ACDC is better depends on system parameters (e.g., number of computing servers) and task parameters (e.g., number of input files), implying that neither CDC nor ACDC is optimal. By merging the ideas of CDC and ACDC, we propose a hybrid scheme and show that it can strictly outperform CDC and ACDC. Furthermore, we derive an information-theoretic converse showing that for the MapReduce task using a type of weakly symmetric Reduce assignment, which includes the Reduce assignments of CDC and ACDC as special cases, the hybrid scheme with a corresponding resource allocation strategy is optimal, i.e., achieves the minimum execution time, for arbitrary amount of computing servers and storage memories.

We consider a MapReduce-type task running in a distributed computing model which consists of K edge computing nodes distributed across the edge of the network and a Master node that assists the edge nodes to compute output functions. The Master node and the edge nodes, both equipped with some storage memories and computing capabilities, are connected through a multicast network. We define the communication time spent during the transmission for the sequential implementation (all nodes send symbols sequentially) and parallel implementation (the Master node can send symbols during the edge nodes’ transmission), respectively. We propose a mixed coded distributed computing scheme that divides the system into two subsystems where the coded distributed computing (CDC) strategy proposed by Songze Li et al. is applied into the first subsystem and a novel master-aided CDC strategy is applied into the second subsystem. We prove that this scheme is optimal, i.e., achieves the minimum communication time for both the sequential and parallel implementation, and establish an optimal information-theoretic tradeoff between the overall communication time, computation load, and the Master node’s storage capacity. It demonstrates that incorporating a Master node with storage and computing capabilities can further reduce the communication time. For the sequential implementation, we deduce the approximately optimal file allocation between the two subsystems, which shows that the Master node should map as many files as possible in order to achieve smaller communication time. For the parallel implementation, if the Master node’s storage and computing capabilities are sufficiently large (not necessary to store and map all files), then the proposed scheme requires at most 1/2 of the minimum communication time of system without the help of the Master node.

Water tanks on the roof of buildings require regular labor-costing inspection, and object detection can be used to automate the task. Current detection frameworks have several drawbacks when they are applied: (1) The output horizontal rectangular boxes cannot provide arbitrary quadrilateral detection representations; (2) False positive results may easily appear when key-point based models are used. In this paper, we propose a novel detection framework: Corner-Line-Prediction, which generates tight quadrilateral detection results of the tank blocks. Our model is built on key point detection network to detect corner points precisely. And an original line predictor is integrated to recognize unique tank edges, such that numerous false positive detections can be suppressed. Experimental results show that our Corner-Line-Prediction (CLP) framework outperforms state- of-the-art detection algorithms in average-precision (AP) and produces better localization results, compared with mainstream general detection models.