Deploying Small Language Models (SLMs) on edge platforms is critical for real-time, privacy-sensitive generative AI, yet constrained by memory, latency, and energy budgets. Quantization reduces model size and cost but suffers from device noise in emerging nonvolatile memories, while conventional memory hierarchies further limit efficiency. SRAM provides fast access but has low density, DRAM must simultaneously accommodate static weights and dynamic KV caches, which creates bandwidth contention, and Flash, although dense, is primarily used for initialization and remains inactive during inference. These limitations highlight the need for hybrid memory organizations tailored to LLM inference. We propose Outlier-aware Quantization with Memory Co-design (QMC), a retraining-free quantization with a novel heterogeneous memory architecture. QMC identifies inlier and outlier weights in SLMs, storing inlier weights in compact multi-level Resistive-RAM (ReRAM) while preserving critical outliers in high-precision on-chip Magnetoresistive-RAM (MRAM), mitigating noise-induced degradation. On language modeling and reasoning benchmarks, QMC outperforms and matches state-of-the-art quantization methods using advanced algorithms and hybrid data formats, while achieving greater compression under both algorithm-only evaluation and realistic deployment settings. Specifically, compared against SoTA quantization methods on the latest edge AI platform, QMC reduces memory usage by 5.5×-7.3×, external data transfers by 7.6×, energy by 11× - 11.7×, and latency by 9.4× - 12.5× when compared to FP16, establishing QMC as a scalable, deployment-ready co-design for efficient on-device inference.

Ensemble learning is a meta-learning approach that combines the predictions of multiple learners, demonstrating improved accuracy and robustness. Nevertheless, ensembling models like Convolutional Neural Networks (CNNs) result in high memory and computing overhead, preventing their deployment in embedded systems. These devices are usually equipped with small batteries that provide power supply and might include energy-harvesting modules that extract energy from the environment. In this work, we propose E-QUARTIC, a novel Energy Efficient Edge Ensembling framework to build ensembles of CNNs targeting Artificial Intelligence (AI)-based embedded systems. Our design outperforms single-instance CNN baselines and state-of-the-art edge AI solutions, improving accuracy and adapting to varying energy conditions while maintaining similar memory requirements. Then, we leverage the multi-CNN structure of the designed ensemble to implement an energy-aware model selection policy in energy-harvesting AI systems. We show that our solution outperforms the state-of-the-art by reducing system failure rate by up to 40% while ensuring higher average output qualities. Ultimately, we show that the proposed design enables concurrent on-device training and high-quality inference execution at the edge, limiting the performance and energy overheads to less than 0.04%.

Intrusion detection systems (IDS) play a crucial role in IoT and network security by monitoring system data and alerting to suspicious activities. Machine learning (ML) has emerged as a promising solution for IDS, offering highly accurate intrusion detection. However, ML-IDS solutions often overlook two critical aspects needed to build reliable systems: continually changing data streams and a lack of attack labels. Streaming network traffic and associated cyber attacks are continually changing, which can degrade the performance of deployed ML models. Labeling attack data, such as zero-day attacks, in real-world intrusion scenarios may not be feasible, making the use of ML solutions that do not rely on attack labels necessary. To address both these challenges, we propose CND-IDS, a continual novelty detection IDS framework which consists of (i) a learning-based feature extractor that continuously updates new feature representations of the system data, and (ii) a novelty detector that identifies new cyber attacks by leveraging principal component analysis (PCA) reconstruction. Our results on realistic intrusion datasets show that CND-IDS achieves up to $6.1 \times$ F-score improvement, and up to $6.5 \times$ improved forward transfer over the SOTA unsupervised continual learning algorithm. Our code is available at https://github.com/Sean-Fuhrman/CND-IDS.

The expansion of edge computing has increased the attack surface, creating an urgent need for robust, real-time machine learning (ML)-based host intrusion detection systems (HIDS) that balance accuracy and efficiency. In such settings, inference latency poses a critical security risk, as delays may provide exploitable opportunities for attackers. However, many state-of-the-art ML-based HIDS solutions rely on computationally intensive architectures with high inference costs, limiting their practical deployment. This paper proposes LIGHT-HIDS, a lightweight machine learning framework that combines a compressed neural network feature extractor trained via Deep Support Vector Data Description (DeepSVDD) with an efficient novelty detection model. This hybrid approach enables the learning of compact, meaningful representations of normal system call behavior for accurate anomaly detection. Experimental results on multiple datasets demonstrate that LIGHT-HIDS consistently enhances detection accuracy while reducing inference time by up to 75× compared to state-of-the-art methods. These findings highlight its effectiveness and scalability as a machine learning-based solution for real-time host intrusion detection.

Large Language Models (LLMs) have recently demonstrated strong potential for cybersecurity question answering (QA), supporting decision-making in real-time threat detection and response workflows. However, their substantial computational demands pose significant challenges for deployment on resource-constrained edge devices. Quantization, a widely adopted model compression technique, can alleviate these constraints. Nevertheless, quantization may degrade model accuracy and increase susceptibility to adversarial attacks. Fine-tuning offers a potential means to mitigate these limitations, but its effectiveness when combined with quantization remains insufficiently explored. Hence, it is essential to understand the trade-offs among accuracy, efficiency, and robustness. We propose AQUA-LLM, an evaluation framework designed to benchmark several state-of-the-art small LLMs under four distinct configurations: base, quantized-only, fine-tuned, and fine-tuned combined with quantization, specifically for cybersecurity QA. Our results demonstrate that quantization alone yields the lowest accuracy and robustness despite improving efficiency. In contrast, combining quantization with fine-tuning enhances both LLM robustness and predictive performance, achieving an optimal balance of accuracy, robustness, and efficiency. These findings highlight the critical need for quantization-aware, robustness-preserving fine-tuning methodologies to enable the robust and efficient deployment of LLMs for cybersecurity QA.

Rich and context-aware activity logs facilitate user behavior analysis and health monitoring, making them a key research focus in ubiquitous computing. The remarkable semantic understanding and generation capabilities of Large Language Models (LLMs) have recently created new opportunities for activity log generation. However, existing methods continue to exhibit notable limitations in terms of accuracy, efficiency, and semantic richness. To address these challenges, we propose DailyLLM. To the best of our knowledge, this is the first log generation and summarization system that comprehensively integrates contextual activity information across four dimensions: location, motion, environment, and physiology, using only sensors commonly available on smartphones and smartwatches. To achieve this, DailyLLM introduces a lightweight LLM-based framework that integrates structured prompting with efficient feature extraction to enable high-level activity understanding. Extensive experiments demonstrate that DailyLLM outperforms state-of-the-art (SOTA) log generation methods and can be efficiently deployed on personal computers and Raspberry Pi. Utilizing only a 1.5B-parameter LLM model, DailyLLM achieves a 17% improvement in log generation BERTScore precision compared to the 70B-parameter SOTA baseline, while delivering nearly 10× faster inference speed.

Detailed activity logs are crucial for health monitoring and personalized interventions. Traditional methods rely on manual editing or raise privacy concerns due to the use of camera recordings. This paper proposes ContextLLM, an innovative system that utilizes a large language model (LLM) to understand sensor data from smartphones and smartwatches and automatically generate contextualized activity logs. Compared to the state-of-the-art, it incorporates key contextual information and physiological indicators, enabling more fine-grained semantic descriptions. Preliminary results show that the automatically generated activity logs achieve 80.26% similarity to human annotations, demonstrating the feasibility.

Learning-based environmental sound recognition has emerged as a crucial method for ultra-low-power environmental monitoring in biological research and city-scale sensing systems. These systems usually operate under limited resources and are often powered by harvested energy in remote areas. Recent efforts in on-device sound recognition suffer from low accuracy due to resource constraints, whereas cloud offloading strategies are hindered by high communication costs. In this work, we introduce ORCA, a novel resource-efficient cloud-assisted environmental sound recognition system on batteryless devices operating over the Low-Power Wide-Area Networks (LPWANs), targeting wide-area audio sensing applications. We propose a cloud assistance strategy that remedies the low accuracy of on-device inference while minimizing the communication costs for cloud offloading. By leveraging a self-attention-based cloud sub-spectral feature selection method to facilitate efficient on-device inference, ORCA resolves three key challenges for resource-constrained cloud offloading over LPWANs: 1) high communication costs and low data rates, 2) dynamic wireless channel conditions, and 3) unreliable offloading. We implement ORCA on an energy-harvesting batteryless microcontroller and evaluate it in a real world urban sound testbed. Our results show that ORCA outperforms state-of-the-art methods by up to 80× in energy savings and 220× in latency reduction while maintaining comparable accuracy.

The Industrial Internet of Things (IIoT) includes networking equipment and smart devices to collect and analyze data from industrial operations. However, IloT security is challenging due to its increased inter-connectivity and large attack surface. Machine learning (ML)-based intrusion detection system (IDS) is an IloT security measure that aims to detect and respond to malicious traffic by using ML models. However, these methods are susceptible to adversarial attacks. In this paper, we propose a RObust Layered DEFense (ROLDEF) against adversarial attacks. Our denoising autoencoder (DAE) based defense approach first detects if a sample comes from an adversarial attack. If an attack is detected, adversarial component is eliminated using the most effective DAE and the purified data is provided to the ML model. We use a realistic IloT intrusion data set to validate the effectiveness of our defense across various ML models, where we improve the average prediction performance by 114% with respect to no defense. Our defense also provides 50 % average prediction performance improvement compared to the state-of-the-art defense under various adversarial attacks. Our defense can also be deployed for any underlying ML model and provides an effective protection against adversarial attacks.

Hyperdimensional Computing (HDC), a promising alternative to address the limitations of edge devices, is not exempt from the security challenges confronted by machine learning algorithms, in particular, adversarial attacks. The limited body of research exploring the security implications of HDC overlooks its inherent algorithm. In this paper, we propose a novel and effective adversarial attack technique targeting HDC. Our approach analyzes and prioritizes the impact of input features as well as encoded elements on decision boundaries and perturbs the input towards incorrect decisions in a guided manner. We evaluate our method on different datasets and attack models (i.e., untargeted/targeted, white-box/gray-box). Experimental results indicate that our proposed design, HDXpose, significantly outperforms the state-of-the-art attack techniques by achieving higher success rate with smaller distortion and execution time, rendering its efficacy for real-time attack generation.

As a lightweight and robust brain-inspired computing paradigm, Hyperdimensional Computing (HDC) serves as a promising solution for the next-generation edge AI. However, the basic form of HDC is vulnerable to privacy leaks and cyber attacks. In this paper, we breifly review and discuss the recent contributions to privacy and security of HDC. We first summarize existing HDC designs to protect against privacy leaks, such as differential privacy. Next, we review the data encryption techniques for collaborative learning using HDC based on Multi-Party Computation and Homomorphic Encryption. Finally, we discuss the HDC-based designs for combating cyber attacks in a malicious environment. More research on private and secure HDC-based methods are needed for future large-scale edge deployment.

Industrial Internet of Things (I-IoT) enables fully automated production systems by continuously monitoring de-vices and analyzing collected data. Machine learning (ML) methods are commonly utilized for data analytics in such systems. Cyberattacks are a grave threat to I-IoT as they can manipu-late legitimate inputs, corrupting ML predictions and causing disruptions in the production systems. Hyperdimensional (HD) computing is a brain-inspired ML method that has been shown to be sufficiently accurate while being extremely robust, fast, and energy-efficient. In this work, we use non-linear encoding-based HD for intelligent fault diagnosis against different adversarial attacks. Our black-box adversarial attacks first train a substitute model and create perturbed test instances using this trained model. These examples are then transferred to the target models. The change in the classification accuracy is measured as the difference before and after the attacks. This change measures the resiliency of a learning method. Our experiments show that HD leads to a more resilient and lightweight learning solution than the state-of-the-art deep learning methods. HD has up to 67.5% higher resiliency compared to the state-of-the-art methods while being up to$25.1\times$faster to train.

The Industrial Internet of Things (I-IoT) enables a smarter maintenance approach for various industrial applications, such as manufacturing, logistics, etc. This approach is based on continuously observing system data to predict device failures and increase device efficiency. This smart maintenance, also known as predictive maintenance (PDM), finds an optimal maintenance schedule to reduce operational and capital costs. Accurate remaining useful life (RUL) prediction is critical for an effective PDM system. Data-driven RUL estimation methods are quite popular owing to their easier implementation. We observe that the performance of data-driven methods varies drastically based on the data set and underlying system parameters, thus making it difficult to have a single algorithm and a parameter set that work best for all settings. We propose an ensemble learning framework, where accurate and diverse base learners are selected out of 20 different state-of-the-art deep learning models. For accuracy, we discover the optimal weights of base learners by constructing an optimization problem. For diversity, we measure the similarity among base learner predictions and iteratively select the most diversified set of models while keeping the accuracy at a certain level. We show that our approach can have 39.2% faster retraining compared to an accuracy-based ensemble with only 3.4% loss in accuracy.