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.

Recent studies have shown that sponge attacks can significantly increase the energy consumption and inference latency of deep neural networks (DNNs). However, prior work has focused primarily on computer vision and natural language processing tasks, overlooking the growing use of lightweight AI models in sensing-based applications on resource-constrained devices, such as those in Internet of Things (IoT) environments. These attacks pose serious threats of energy depletion and latency degradation in systems where limited battery capacity and real-time responsiveness are critical for reliable operation. This paper makes two key contributions. First, we present the first systematic exploration of energy-latency sponge attacks targeting sensing-based AI models. Using wearable sensing-based AI as a case study, we demonstrate that sponge attacks can substantially degrade performance by increasing energy consumption, leading to faster battery drain, and by prolonging inference latency. Second, to mitigate such attacks, we investigate model pruning, a widely adopted compression technique for resource-constrained AI, as a potential defense. Our experiments show that pruning-induced sparsity significantly improves model resilience against sponge poisoning. We also quantify the trade-offs between model efficiency and attack resilience, offering insights into the security implications of model compression in sensing-based AI systems deployed in IoT environments.

The rapid growth of machine learning (ML) technologies has raised significant concerns about their environmental impact, particularly regarding energy consumption and carbon emissions. This comprehensive review examines the intersection of ML and sustainability, synthesizing research from 2014 to 2024 to provide a holistic view of sustainable ML practices. This systematic review, encompassing over 200 peer-reviewed publications, reveals a growing emphasis on quantifying and mitigating the environmental footprint of ML systems. Key findings include: (1) a 300% increase in sustainable ML research since 2020; (2) the emergence of specialized carbon footprint quantification tools for ML; and (3) promising advancements in energy-efficient algorithms and green computing infrastructure. This research identifies critical challenges, including the lack of standardized sustainability metrics and the need for more robust life-cycle assessments of ML systems. The review also highlights the potential of transfer learning, federated learning, and hardware innovations in reducing ML's environmental impact. The analysis culminates in a novel framework for implementing sustainable practices in ML projects and a detailed roadmap for future research. This work provides researchers, practitioners, and policymakers with crucial insights to drive the development of more environmentally responsible ML technologies, ultimately contributing to global sustainability goals.

With the proliferation of edge devices, there is a significant increase in attack surface on these devices. The decen-tralized deployment of threat intelligence on edge devices, coupled with adaptive machine learning techniques such as the in-context learning feature of Large Language Models (LLMs), represents a promising paradigm for enhancing cybersecurity on resource-constrained edge devices. This approach involves the deployment of lightweight machine learning models directly onto edge devices to analyze local data streams, such as network traffic and system logs, in real-time. Additionally, distributing computational tasks to an edge server reduces latency and improves responsiveness while also enhancing privacy by processing sensitive data locally. LLM servers can enable these edge servers to autonomously adapt to evolving threats and attack patterns, continuously updating their models to improve detection accuracy and reduce false positives. Furthermore, collaborative learning mechanisms facilitate peer-to-peer secure and trustworthy knowledge sharing among edge devices, enhancing the collective intelligence of the network and enabling dynamic threat mitigation measures such as device quarantine in response to detected anomalies. The scalability and flexibility of this approach make it well-suited for diverse and evolving network environments, as edge devices only send suspicious information such as network traffic and system log changes, offering a resilient and efficient solution to combat emerging cyber threats at the network edge. Thus, our proposed framework can improve edge computing security by providing better security in cyber threat detection and mitigation by isolating the edge devices from the network.

Cyber-physical systems have become an essential part of the modern healthcare industry. The healthcare cyber-physical systems (HCPS) combine physical and cyber components to improve the healthcare industry. While H CPS has many advantages, it also has some drawbacks, such as a lengthy data entry process, a lack of real-time processing, and limited real-time patient visualization. To overcome these issues, this paper represents an innovative approach to integrating large language model (LLM) to enhance the efficiency of the healthcare system. By incorporating LLM at various layers, HCPS can leverage advanced AI capabilities to improve patient outcomes, advance data processing, and enhance decision-making.

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,