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.

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.