edge system engineers

Abstract: Multi-agent large language model (LLM) systems are increasingly deployed at the edge to support complex tasks that require collaboration and information exchange among multiple agents. However, when task contexts are passed across agents, shared context that has already been processed by upstream agents often needs to be prefilled again, resulting in substantial redundant computation. Reusing an already prefilled key--value (KV) cache can avoid this recomputation, but requires the reusable KV state to be transferred across the network before the receiving agent can begin generation. Under strict time-to-first-token (TTFT) deadlines and unreliable edge links, delivering the complete KV cache may be infeasible, making it necessary to prioritize and protect the most valuable KV information within a limited transmission budget. This paper proposes PriorityFEC, a utility-aware KV-cache handoff scheme that jointly exploits model-side KV importance and transport-side reliability. PriorityFEC first constructs a utility-aware source stream by placing high-utility KV blocks at earlier transmission positions. It then protects the ordered stream using a feedback-adaptive Streaming Forward Erasure Correction (FEC) mechanism, which schedules repair packets according to receiver feedback before the TTFT deadline. We implement PriorityFEC on a distributed edge LLM serving prototype and evaluate it with Qwen2.5-7B-Instruct and Llama-3.1-8B-Instruct across multiple datasets. Results show that PriorityFEC achieves superior task quality over the compared transport schemes, with its advantages becoming particularly pronounced under stringent deadline constraints. These results demonstrate the importance of jointly optimizing KV-cache utility and deadline-aware transport reliability when reusable LLM state must be handed off across unreliable edge networks.

Abstract: The increasing adoption of the Internet of Things (IoT) and cloud computing has accelerated the evolution of edge computing paradigms [1]. Industry forecasts estimate that the number of connected IoT devices will reach approximately 50 billion by 2030, following an estimated 38 billion connections by 2025 [34], resulting in an unprecedented growth in data generation. This trend necessitates efficient, scalable, and secure data processing mechanisms at the network edge. Consequently, ensuring the reliable management and protection of IoT applications and devices has become a critical challenge. In this context, KubeEdge extends cloud-native capabilities to edge environments, enabling distributed orchestration while introducing new security concerns. This paper investigates the security of container images in IoT-driven and distributed edge architectures. Specifically, we analyze the impact of major security threats, including Denial of Service (DoS) attacks and malicious container deployments, on the availability and operational stability of KubeEdge-based systems. To address these challenges, we propose a lightweight Recommended Intrusion Detection Rule Set (RIDRS) tailored for resource-constrained edge environments. The proposed approach improves system resilience by enabling timely detection and mitigation of security threats. We define system stability as the ability to maintain consistent operational behavior and to recover autonomously under adversarial conditions. Experimental results demonstrate that RIDRS significantly reduces system downtime and enhances service availability, particularly in scenarios involving code injection and malicious pod deployment attacks.

Abstract: This paper proposes FREDI (Fair Resource Allocation for Edge Dual-Threshold Inference), a secure wireless edge-intelligence framework for event-triggered inference in a cooperative user equipment (UE)--edge server (ES)--cloud system. Each UE performs early-exit convolutional neural network (CNN) screening using dual confidence thresholds, while critical events are securely offloaded to an edge server for detailed classification. We formulate a proportionally-fair utility maximization problem that jointly optimizes UE--ES association, wireless and processing resources, and confidence thresholds. FREDI decomposes the problem into proportional-fair resource allocation and dual-threshold inference optimization. We prove that the detected-critical event set is set-monotone non-increasing in both thresholds, and exploit the finite empirical confidence domain for exact threshold optimization. An empirical resource--utility response envelope yields a computable global suboptimality bound and a sufficient condition for global optimality. By pre-eliminating infeasible UE--ES pairs and exactly projecting out bandwidth and transmit-power variables, the resource-allocation subproblem is reduced to a mixed-integer exponential-cone program solvable to the certified global optimality within a prescribed gap. Numerical results with early-exit MobileNetV2 and ShuffleNetV2 demonstrate near-perfect UE fairness with aggregate utility close to a Sum-Utility benchmark, reveal security-induced resource fragmentation, and demonstrate the Stage-A scalability from 6 to 144 UEs with median solving time below 0.1~s in the tested configurations.

Abstract: Centralized learning is a fundamental paradigm in modern AI, in which data are collected from distributed edge devices and aggregated at a central host for model training. However, this pipeline is often bottlenecked by the substantial communication overhead of data collection. Dataset Distillation (DD), with its high compression ratio, is therefore attractive for centralized learning on distributed data. Yet, the cost efficiency of DD in non-uniform edge environments remains largely unexplored. Geographically distributed edge devices face different energy and data-transfer prices, while key DD hyperparameters, such as the target compression ratio and number of distillation steps, substantially affect energy use, transfer overhead, and downstream test accuracy. These hyperparameters must therefore be tuned both locally and globally. We propose the Cost-Efficient Dataset Distillation optimizer (CEDD-optimizer), a hyperparameter-tuning framework for cost-efficient distributed DD. It minimizes total cost under a training-quality constraint by optimizing hyperparameter settings across edge devices while accounting for environmental non-uniformity. The framework comprises two modules: CEDD-calibrator and CEDD-solver. CEDD-calibrator identifies parameters for our energy and training-quality models: the former is determined through offline calibration, whereas the latter is estimated online through a three-step tuning scheme. Based on these models, CEDD-solver solves the cost-minimization problem to steer and improve the distributed DD workflow. Experiments across various image datasets show that our approach achieves up to a 20.8 improvement over the baseline DD method under the same quality constraint.