With the advancement of artificial intelligence, vision-language models (VLMs) that integrate text and image modalities have become central to multimodal learning and are increasingly deployed in Internet of Things (IoT) environments such as smart surveillance, autonomous driving and industrial monitoring. However, VLMs are also highly susceptible to adversarial attacks. Most previous black-box attack methods towards the VLMs rely on either the soft label or substitute models. However, most of them need detailed information on the target model, which is often unavailable for real-world threat models due to limitations on additional access and resource restrictions. In this study, we propose a surrogate-free hard-label attack towards the VLMs, which does not require a substitute model, called Hard-Label Fusion Attack towards Vision-Language models. HFAVL directly leverages the model’s final decision to jointly generate the perturbation for both text and image modalities instead of the confidence score necessary for the soft label attack. By transforming the text into a continuous embedding space that enables them to be aligned with the image in a unified manner, HFAVL applies cooperative Monte Carlo-based estimation on the multimodal gradient. Aiming to improve the efficiency of attacks, we introduce a Mutual Iterative Refinement mechanism (MIR), which gradually searches for a better adversarial example through small iterative steps that blend perturbations across both modalities. Additionally, to alleviate the imbalance between textual and visual noise during the attack, we propose a new multimodal edit distance evaluation metric to measure the quality of multimodal adversarial examples. Extensive experimental results demonstrate that HFAVL consistently outperforms existing multimodal adversarial attack methods across various vision-language pretraining (VLP) models. Notably, under the setting with recall@10, HFAVL achieves over 80% attack success rate on most benchmarks, demonstrating its efficiency and high precision in generating effective adversarial examples.

Backdoor attacks pose severe security challenges to federated learning systems due to their stealthy nature. Existing detection methods primarily focus on identifying anomalies by analyzing discrepancies in client model updates. However, in federated learning, the non-independent and identically distributed (non-IID) nature of client data leads to inconsistencies among local model updates, which can mask the distinguishing features of backdoor attacks and consequently degrade the performance of detection methods. Unlike benign models, which are trained solely for a single classification task, backdoored models are simultaneously optimized for both the classification (main) task and the backdoor task. Therefore, training the backdoored models can be regarded as a multi-task learning problem. Inspired by information bottleneck theory, we observe that backdoored models exhibit more stable feature representations than benign models when performing the main task. Based on this insight, we propose a novel stability metric that quantitatively captures the disparity in feature map stability between backdoored and benign models. Leveraging this metric, we develop a new backdoor detection framework for federated learning. Our method computes anomaly scores for each client and selectively aggregates models with benign characteristics, effectively defending against backdoor attacks. We validate our approach through extensive experiments on multiple benchmark datasets under non-IID settings. The results demonstrate that our method consistently achieves high detection performance across a range of backdoor scenarios and data heterogeneity levels.

Serverless computing has emerged as a novel paradigm in cloud computing, characterized by its agile scalability, cost-effective pay-as-you-go billing, and user-friendly capabilities for Machine Learning (ML) inference tasks. Developers wrap their ML algorithms into serverless functions and run them in containers. However, the well-known cold-start problem significantly slows down the response time of functions. To address cold-starts, the technique of pre-warming, which proactively maintains containers in a warm state, has gained widespread adoption across both research and industry. Nevertheless, we observed that pre-warming does not address the distinct delays caused by the loading of ML artifacts. According to our analysis, in ML inference functions, the time required to load libraries and models significantly exceeds the time needed to warm containers. Thus, relying solely on pre-warming is insufficient for mitigating cold-starts. This paper presentsTyche, an opportunistic pre-loading approach designed to eliminate the latency associated with loading ML artifacts, enabling near-instant inference and minimizing function execution time.Tychefully leverages the idle memory in warmed containers and GPUs to pre-load required libraries and models, striking an optimal balance between acceleration and resource efficiency. Additionally,Tycheis tailored for large-scale serverless platforms, incorporating cluster-wide scheduling and lightweight locality-aware load balancing to enhance performance. We designTycheto be transparent to providers and compatible with existing pre-warming solutions. Experiments on OpenWhisk with real-world workloads show thatTychereduces up to 93% loading latency and achieves up to 8× speedup compared to state-of-the-art pre-warming solutions. Compared with the state-of-the-art serverless pre-loading solution,Tychealso achieves up to 1.9× speedup.

The rapid growth of industrial Internet of Things (IoT) devices presents significant opportunities for federated learning to advance secure machine learning in industrial IoT systems. However, the performance of global federated learning models deteriorates significantly due to data heterogeneity inherent in industrial IoT systems. To address this issue, we propose a novel personalized federated learning framework via fine-grained one-shot clustering (FedFOC). Our method simultaneously considers the dataset structure and the relative positions of samples to extract compact features for clustering. Specifically, we combine clients’ locally calculated sample-level distances with MinHash signatures of their label sets to transform their data distributions into 1-D vectors to reduce communication costs. These vectors are then clustered to identify the fine-grained similarities between clients’ data distributions. To capture the complex relationships among local data distributions, we perform soft clustering in a one-shot manner and quantify client cluster association strengths. Subsequently, the clustering results are utilized to improve the local models’ personalized performance. Extensive experiments conducted on four widely used public datasets under various heterogeneous scenarios demonstrate that FedFOC outperforms state-of-the-art personalized federated learning methods in the accuracy of local models in almost all scenarios partitioned by Dirichlet. In addition, we also validate the effectiveness of our approach on the industrial dataset, indicating that our method has potential value in real-world applications.

Serverless computing has rapidly prospered as a new cloud computing paradigm with agile scalability, pay-as-you-go pricing, and ease-to-use features for Machine Learning (ML) inference tasks. Users package their ML code into lightweight serverless functions and execute them using containers. Unfortunately, a notorious problem, called cold-starts, hinders serverless computing from providing low-latency function executions. To mitigate cold-starts, pre-warming, which keeps containers warm predictively, has been widely accepted by academia and industry. However, pre-warming fails to eliminate the unique latency incurred by loading ML artifacts. We observed that for ML inference functions, the loading of libraries and models takes significantly more time than container warming. Consequently, pre-warming alone is not enough to mitigate the ML inference function's cold-starts.