Cyber-Physical Systems (CPS) are increasingly leveraging Federated Learning (FL) to enable decentralized intelligence while preserving privacy across distributed devices. Federated adversarial learning (FAL) leverages FL and adversarial training to enhance model robustness against adversarial attacks while maintaining data privacy across decentralized, heterogeneous devices. While FAL strengthens CPS resilience against adversarial threats, variations in energy constraints, carbon emissions, computational capabilities, and latency requirements introduce additional complexity. These variations impact energy consumption, carbon emissions, and power source efficiency, creating a complex trade-off between sustainability and robustness. This underscores the critical need for standardized metrics to systematically evaluate and balance these competing factors in FAL-enabled CPS. In this paper, we propose three novel robustness metrics designed to quantify the interplay between energy efficiency, sustainability dimensions, and adversarial robustness in FAL setups for CPS. The proposed methodology accounts for diverse CPS scenarios, addressing factors such as emissions, energy consumption, latency, renewable energy, and low-energy devices with critical latency needs. We validate our approach through simulations in two setups, including a single-device environment to isolate device variability and a heterogeneous multi-device environment to evaluate architectural impacts. The results demonstrate the effectiveness of our proposed metrics in systemically quantifying the trade-off between sustainability and robustness in FAL-based CPS.

Large Vision-Language Models (LVLMs) have emerged as transformative tools in multimodal tasks, seamlessly integrating pretrained vision encoders to align visual and textual modalities. Prior works have highlighted the susceptibility of LVLMs to dual exploits (gradient-based and optimization-based jailbreak attacks), which leverage the expanded attack surface introduced by the image modality. Despite advancements in enhancing robustness, existing methods fall short in their ability to defend against dual exploits while preserving fine-grained semantic details and overall semantic coherence under intense adversarial perturbations. To bridge this gap, we introduce SheiLding against Dual Exploits (SLADE), a novel unsupervised adversarial fine-tuning scheme that enhances the resilience of CLIP-based vision encoders. SLADE’s dual-level contrastive learning approach balances the granular and the holistic, capturing fine-grained image details without losing sight of high-level semantic coherence. Extensive experiments demonstrate that SLADE-equipped LVLMs set a new benchmark for robustness against dual exploits while preserving fine-grained semantic details of perturbed images. Notably, SLADE achieves these results without compromising the core functionalities of LVLMs, such as instruction following, or requiring the computational overhead (e.g., large batch sizes, momentum encoders) commonly associated with traditional contrastive learning methods.

Vision-Language Models (VLMs) have achieved remarkable performance in various downstream tasks, such as image captioning and visual question answering (VQA), by learning joint representations from paired image-text data. However, the robustness of VLMs against adversarial attacks is a critical concern as these models become widely adopted in real-world applications. This study provides a comprehensive overview of the current state of research on the adversarial robustness of VLMs. We introduce a novel taxonomy that categorizes the diverse landscape of adversarial attacks on VLMs, considering factors such as the modality of the attack, the level of model access, and the attack objective. We explore various types of attacks, including jailbreak, backdoor, data poisoning, gradient-based, and patch-based attacks, and discuss their underlying principles and assumptions. Furthermore, we present a comparative analysis of existing defense strategies and their strengths and limitations. By identifying open challenges and future research directions, we aim to stimulate further exploration of this crucial area and contribute to the development of robust and trustworthy VLMs for real-world applications.

Stress is a major factor affecting mental health and long-term well-being, making its early detection essential for effective intervention. Wearable sensors have emerged as a promising solution for continuous stress monitoring, but current systems face two major limitations. First, they are single-dimensional, relying on either chest- or wrist-based sensors alone, which limits their ability to capture diverse physiological signals. Second, they typically require centralized data collection, raising significant privacy concerns since stress-related information is highly sensitive. This work introduces a distributed multimodal stress monitoring framework that addresses both challenges. The framework integrates synchronized signals from both chest and wrist sensors, providing a richer and more reliable representation of stress patterns. An attention-based fusion mechanism is introduced to adaptively weigh complementary information across modalities, while two deep learning architectures, BiLSTM and TS-BERT, are employed for temporal modeling. To ensure scalability and protect user privacy, we adopt Federated Learning (FL) for decentralized training across simulated edge devices. Experimental results demonstrate that TS-BERT achieves superior performance, reaching 99.93% test accuracy in FL, closely matching centralized outcomes.

Medical image classification plays a vital role in disease diagnosis, but often suffers from class imbalance and privacy concerns, particularly in rare disease categories. Such an imbalance can bias machine learning models toward majority classes, reducing the diagnostic accuracy for minority conditions across geographically diverse populations. To address these challenges, we propose integrating the synthetic minority oversampling technique (SMOTE) into the federated learning (FL) framework for 2D medical image analysis, enabling privacy-preserving and balanced learning. The SMOTE technique enhances neural networks’ generalization by generating informative synthetic samples, outperforming raw duplication, and reducing overfitting. In our approach, by operating on features obtained from deep models like VGG19, we avoid pixel-level interpolation artifacts and retain critical semantic information. The SMOTE technique is applied to the extracted features at the local client level, where it generates new samples for the minority class by interpolating between existing samples to achieve the local data balance. The proposed approach allows each client to balance its local data without compromising privacy, promoting fair learning across all classes in disease classification tasks. We evaluate our approach on two publicly available chest X-ray datasets for binary and multi-class classifications. Our method achieves 83% accuracy and 97% recall in binary classification and 50% accuracy in multiclass settings, highlighting its potential to contribute to diagnostic fairness and robustness in healthcare applications.

Large Vision-Language Models (LVLMs), trained on multimodal big datasets, have significantly advanced AI by excelling in vision-language tasks. However, these models remain vulnerable to adversarial attacks, particularly jailbreak attacks, which bypass safety protocols and cause the model to generate misleading or harmful responses. This vulnerability stems from both the inherent susceptibilities of LLMs and the expanded attack surface introduced by the visual modality. We propose SimCLIP+, a novel defense mechanism that adversarially fine-tunes the CLIP vision encoder by leveraging a Siamese architecture. This approach maximizes cosine similarity between perturbed and clean samples, facilitating resilience against adversarial manipulations. Sim-CLIP+ offers a plug-and-play solution, allowing seamless integration into existing LVLM architectures as a robust vision encoder. Unlike previous defenses, our method requires no structural modifications to the LVLM and incurs minimal computational overhead. Sim-CLIP+ demonstrates effectiveness against both gradient-based adversarial attacks and various jailbreak techniques. We evaluate Sim-CLIP+ against three distinct jailbreak attack strategies and perform clean evaluations using standard downstream datasets, including COCO for image captioning and OKVQA for visual question answering. Extensive experiments demonstrate that Sim-CLIP+ maintains high clean accuracy while substantially improving robustness against both gradient-based adversarial attacks and jailbreak techniques.

Federated Learning (FL) has recently experienced tremendous popularity due to its emphasis on user data privacy. However, the distributed computations of FL can result in constrained communication and drawn-out learning processes, necessitating the client-server communication cost optimization. The ratio of chosen clients and the quantity of local training passes are two hyperparameters that have a significant impact on the performance of FL. Due to different training preferences across various applications, it can be difficult for FL practitioners to manually select such hyperparameters. In this paper, we introduce FedAVO, a novel FL algorithm that enhances communication effectiveness by selecting the best hyperparameters leveraging the African Vulture Optimizer (AVO). Our research demonstrates that the communication costs associated with FL operations can be substantially reduced by adopting AVO for FL hyperparameter adjustment. Through extensive evaluations of FedAVO on benchmark datasets, we identify the optimal hyperparameters that are appropriately fitted for the benchmark datasets, eventually increasing global model accuracy by 6% in comparison to the state-of-the-art FL algorithms (such as FedAvg, FedProx, FedPSO). The code, data, and experiments have been made publicly available on our GitHub repository11https://github.com/speedlab-git/FedAVO.

The prevalence of road and highway accidents, largely attributed to driver distraction, highlights the critical need for an intelligent system that can assess driver alertness and provide timely alerts. Current solutions in the market are often characterized by their high costs and complex installation processes, which significantly limit their accessibility and practical application on a broader scale. In response to this challenge, our research introduces a cost-effective, real-time framework designed to monitor driver alertness utilizing the Raspberry Pi, a device known for its limited processing capabilities. This inherent limitation prompted us to implement several optimizations, which are elaborated upon within our study, to equip the Raspberry Pi with the ability to make real-time decisions effectively. Our proposed approach features a novel algorithm that integrates Haar Cascade and facial landmark detection techniques, enabling the rapid and precise identification of facial features, thereby surpassing the accuracy of existing leading methods. This system meticulously tracks facial points to evaluate driver attentiveness through indicators such as drowsiness, yawning, and unusual facial movements. It utilizes metrics including the Eye Aspect Ratio (EAR), Lips Movement Ratio (LMR), and Face Position Difference (FPD) to initiate driver alerts, thereby contributing to the prevention of potential accidents. Furthermore, our system is enhanced with a GSM module, facilitating emergency notifications to the vehicle owner in critical situations. Extensive testing of our framework, involving participants of diverse sizes, skin colors, and ages, has demonstrated its efficacy in sustaining driver awareness with minimal processing delays, even when deployed on devices with limited computational resources. This affirms the potential of our proposed solution to serve as a viable and scalable option for enhancing road safety through improved driver alertness monitoring.

In the era of Large Language Models (LLMs), the application of advanced AI technologies to data captured by wearables devices, combined with the fusion of contextual data, presents a revolutionary approach to enhancing real-time public safety, individual security, and emergency response. In this paper, we introduce WatchOverGPT, a novel framework that leverages this integration to promptly identify and respond to potential life-threatening criminal activities and safety concerns. WatchOverGPT combines the capabilities of wearable cameras, smartphones' location data, and LLM-based advanced con-versational AI communication through Generative Pre-trained Transformer (GPT). The core of this framework involves a wearable camera connected to the user's smartphone, which continuously captures and analyzes the environment for signs of distress or criminal behaviors, including human actions and the presence of weapons, coupled with location and other information from the smartphone by which GPT-based application provides an autonomous decision-making process. This paper explores the framework's design, implementation, and potential impact of LLM applications on public safety. The proposed framework aims to bridge the gap between safety threats and emergency response teams in the fight against crime through real-time data processing and AI -driven autonomous communication, enhancing the security of individuals in various settings,

The widespread popularity of Federated Learning (FL) has led researchers to delve into its various facets, primarily focusing on personalization, fair resource allocation, privacy, and global optimization, with less attention puts towards the crucial aspect of ensuring efficient and cost-optimized communication between the FL server and its agents. A major challenge in achieving successful model training and inference on distributed edge devices lies in optimizing communication costs amid resource constraints, such as limited bandwidth, and selecting efficient agents. In resource-limited FL scenarios, where agents often rely on unstable networks, the transmission of large model weights can substantially degrade model accuracy and increase communication latency between the FL server and agents. Addressing this challenge, we propose a novel strategy that integrates a knowledge distillation technique with a Particle Swarm Optimization (PSO)-based FL method. This approach focuses on transmitting model scores instead of weights, significantly reducing communication overhead and enhancing model accuracy in unstable environments. Our method, with potential applications in smart city services and industrial IoT, marks a significant step forward in reducing network communication costs and mitigating accuracy loss, thereby optimizing the communication efficiency between the FL server and its agents.

The recent advancement of pretrained models shows great potential as well as challenges for privacy-preserving distributed machine learning technique called Federated Learning (FL). With the growing demands of foundation models, it is now an urgent need to explore the potential of such foundation models in a distributed setting. In this paper, In this paper, we delve into the complexities of leveraging foundation models, like CLIP into FL frameworks to preserve data privacy, and efficiently training distributed network clients across heterogeneous data landscapes. We specifically aim to address the issues related to non-IID data distributions, skewed class representation of FL clients' local dataset, communication overhead and high resource consumption due to large, complex model training in an FL setting. To address these, we propose TriplePlay, a framework that tailors CLIP foundation model as an adapter to strengthen FL model's performance and adaptability across heterogeneous data distributions among the clients. Besides, we address the long-tail distribution problem in an FL environment to maintain fairness and optimize the computational resource demands of the FL clients through quantization and low-rank adaptation techniques. A comprehensive simulations results with two distinct datasets and different FL settings demonstrate that TriplePlay efficiently reduces GPU usage and accelerates the convergence time that ultimately reduces the communication cost.

Differentially-Private Federated Learning (DPFL) is an emerging privacy-preserving distributed machine learning paradigm that allows for the automatic recognition of human activities using wearable sensors without compromising users’ sensitive data. However, this decentralized approach makes the system vulnerable to poisoning attacks, where malicious agents can inject contaminated data during local model training. This paper presents the results of our research on designing, developing, and evaluating a holistic model for data poisoning attacks in DPFL-based human activity recognition (HAR) systems. Specifically, we focus on label-flipping poisoning attacks, where the label of a sensor reading is maliciously changed during data collection. To investigate the impact of such attacks, we develop a simulator that explores key design issues, such as the correlation between the level of differential privacy, the level of poisoning, the number of communication rounds, and the number of agents in the system. Our findings shed light on the effectiveness of label contamination attacks in DPFL-based HAR systems and can inform the development of more robust and secure models.

Federated learning (FL) has gained widespread popularity in recent years due to the fast booming of advanced machine learning and artificial intelligence, along with emerging security and privacy threats. Federated learning (FL) enables efficient model generation from local data storage of the edge devices without revealing the sensitive data to any entities. While this paradigm partly mitigates the privacy issues of users’ sensitive data, the performance of the FL process can be threatened and reach a bottleneck due to the growing cyber threats and privacy violation techniques. To expedite the proliferation of the FL process, the integration of blockchain for FL environments has drawn increasing attention from academia and industry. Blockchain has the potential to prevent security and privacy threats with its decentralization, immutability, consensus, and transparency characteristics. However, if the blockchain mechanism requires costly computational resources, then the resource-constrained FL clients cannot be involved in the training. Considering that, this survey focuses on reviewing the challenges, solutions, and future directions for the successful deployment of blockchain in resource-constrained FL environments. We comprehensively review variant blockchain mechanisms suitable for the FL process and discuss their tradeoffs for a limited resource budget. Furthermore, we extensively analyze the cyber threats that could be observed in a resource-constrained FL environment, and how blockchain can play a key role in blocking those cyberattacks. To this end, we highlight some potential solutions for the coupling of blockchain and FL that can offer high levels of reliability, data privacy, and distributed computing performance.

Federated learning (FL) is a distributed machine learning strategy that generates a global model by learning from multiple decentralized edge clients. FL enables on-device training, keeping the client’s local data private, and further, updating the global model based on the local model updates. While FL methods offer several advantages, including scalability and data privacy, they assume there are available computational resources at each edge-device/client. However, the Internet-of-Things (IoT)-enabled devices, e.g., robots, drone swarms, and low-cost computing devices (e.g., Raspberry Pi), may have limited processing ability, low bandwidth and power, or limited storage capacity. In this survey article, we propose to answer this question: how to train distributed machine learning models for resource-constrained IoT devices? To this end, we first explore the existing studies on FL, relative assumptions for distributed implementation using IoT devices, and explore their drawbacks. We then discuss the implementation challenges and issues when applying FL to an IoT environment. We highlight an overview of FL and provide a comprehensive survey of the problem statements and emerging challenges, particularly during applying FL within heterogeneous IoT environments. Finally, we point out the future research directions for scientists and researchers who are interested in working at the intersection of FL and resource-constrained IoT environments.

Nowadays, there is an ever-increasing deployment of intelligent edge devices, such as smartphones, wearable devices, and autonomous vehicles. It is enabled by the integration of advanced sensors with higher computing capabilities and widespread internet availability. These edge devices generate a vast amount of data that can be utilized for better inference. However, due to privacy concerns, communication overhead, processing delay, and security issues, traditional machine learning (ML) algorithms face challenges that work in a centralized fashion where all the available data is accumulated beforehand. Federated learning (FL) is a new distributed on-device learning method that generates a global model through the collaboration of edge devices without compromising data privacy. In this paper, we propose a federated transfer learning (FTL) model considering clients’ heterogeneity in terms of their available computing resources and model architecture. We simulate the training performance of heterogeneous clients and observe that clients with sufficient resources require significantly lower computational time. In turn, the resource-constrained clients take notably higher computational time to accomplish a given task. Inspired by that, we design an FL model that constructs multiple global models based on the available resources of the clients and carries out a separate training process for each of the global models. We demonstrate the effectiveness of our proposed strategy by evaluating our FL model on CFAR100 dataset. Our findings show that training time differs significantly among the heterogeneous clients and assigning multiple global models can notably improve convergence time.

Smartphones, autonomous vehicles, and the Internet-of-things (IoT) devices are considered the primary data source for a distributed network. Due to a revolutionary breakthrough in internet availability and continuous improvement of the IoT devices capabilities, it is desirable to store data locally and perform computation at the edge, as opposed to share all local information with a centralized computation agent. A recently proposed Machine Learning (ML) algorithm called Federated Learning (FL) paves the path towards preserving data privacy, performing distributed learning, and reducing communication overhead in large-scale machine learning (ML) problems. This paper proposes an FL model by monitoring client activities and leveraging available local computing resources, particularly for resource-constrained IoT devices (e.g., mobile robots), to accelerate the learning process. We assign a trust score to each FL client, which is updated based on the client's activities. We consider a distributed mobile robot as an FL client with resource limitations either in memory, bandwidth, processor, or battery life. We consider such mobile robots as FL clients to understand their resource-constrained behavior in a real-world setting. We consider an FL client to be untrustworthy if the client infuses incorrect models or repeatedly gives slow responses during the FL process. After disregarding the ineffective and unreliable client, we perform local training on the selected FL clients. To further reduce the straggler issue, we enable an asynchronous FL mechanism by performing aggregation on the FL server without waiting for a long period to receive a particular client's response.

The Internet-of-things (IoT) devices and technologies led to a revolutionary breakthrough over the data collection procedure and Machine Learning (ML) approaches of a distributed network. It is preferable to store sensitive data on-device without sharing with a centralized computation agent and carry-out computation at the edge devices to ensure security and privacy. A recently invented distributed ML technique, Federated Learning (FL) holds the same theme that allows the edge devices to perform training on their edges and obtains a final model by learning from the model information of all the distributed edge clients. As the clients' raw data remain at local and models are generated on clients' edge, so it enhances security, privacy, and reduces computation cost in large-scale ML problems. The FL technique deals with various distributed clients that may have statistical heterogeneity and systems heterogeneity. This paper aims at dealing with such heterogeneity within an FL environment by monitoring each client's activities and leveraging resources based on the required computation during the model training phase. For each training round, we consider the proficient and trustworthy client by inspecting their resource-availability and previous history. We assign a trust score to each client based on their performance and update that score after each training period. To bring systems heterogeneity within our FL environment, we consider distributed mobile robots as FL clients with heterogeneous system configurations in terms of memory, processor, bandwidth, or battery life to understand their performance and resource-constraint behavior in a real-world setting. We filter-out the weak clients who cannot perform computation based on their available resources and exclude the untrustworthy clients who has previous record of repeatedly infusing incorrect or diverge model information, or, become stragglers during FL training. After eliminating the stragglers and untrustworthy FL clients, we conduct local training on each selected FL clients. To further mitigate the straggler issue, we enable asynchronous FL technique that can handle the clients' variant response time and continue FL training without waiting for a particular client for a long period.

Recently, blockchain has received much attention from the mobility-centric Internet of Things (IoT). It is deemed the key to ensuring the built-in integrity of information and security of immutability by design in the peer-to-peer network (P2P) of mobile devices. In a permissioned blockchain, the authority of the system has control over the identities of its users. Such information can allow an ill-intentioned authority to map identities with their spatiotemporal data, which undermines the location privacy of a mobile user. In this paper, we study the location privacy preservation problem in the context of permissioned blockchain-based IoT systems under three conditions. First, the authority of the blockchain holds the public and private key distribution task in the system. Second, there exists a spatiotemporal correlation between consecutive location-based transactions. Third, users communicate with each other through short-range communication technologies such that it constitutes a proof of location (PoL) on their actual locations. We show that, in a permissioned blockchain with an authority and a presence of a PoL, existing approaches cannot be applied using a plug-and-play approach to protect location privacy. In this context, we propose BlockPriv, an obfuscation technique that quantifies, both theoretically and experimentally, the relationship between privacy and utility in order to dynamically protect the privacy of sensitive locations in the permissioned blockchain.