Federated Learning (FL) offers a promising way to train machine learning models collaboratively on decentralized edge devices, addressing key privacy, communication, and regulatory challenges in smart city environments. This survey adopts a narrative approach, guided by systematic review principles such as PRISMA and Kitchenham, to synthesize current FL research in urban contexts. Unlike prior domain-focused surveys, this work introduces a challenge-oriented taxonomy and integrates an explicit analysis of reproducibility, including datasets and deployment artifacts, to assess real-world readiness. The review begins by examining how FL supports the privacy-preserving analysis of environmental and mobility data. It then explores strategies for resource optimization, including load balancing, model compression, and hierarchical aggregation. Applications in anomaly and event detection across power grids, water infrastructure, and surveillance systems are also discussed. In the energy sector, the survey emphasizes the role of FL in demand forecasting, renewable integration, and sustainable logistics. Particular attention is given to security issues, including defenses against poisoning attacks, Byzantine faults, and inference threats. The study identifies ongoing challenges such as data heterogeneity, scalability, resource limitations at the edge, privacy–utility trade-offs, and lack of standardization. Finally, it outlines a structured roadmap to guide the development of reliable, scalable, and sustainable FL solutions for smart cities.

The rapid implementation of multi-cloud architectures, such as the integration of services from multiple cloud providers, gives organizations enhanced flexibility, resilience, and vendor independence. However, the multi-cloud model presents complicated security challenges due to diverse platforms, fragmented governance, and increased areas of concern. This paper presents a comprehensive literature review of the multi-cloud environment arena, focusing on the analysis of threats, vulnerabilities, cost optimization, mitigation strategies, and research trends. It covers a comprehensive range of risks, including data breaches, insider threats, API exploitation, configuration errors, and emerging multi-vector attacks, as well as the cumulative complexity of aligning policies, managing identities, and ensuring compliance across diverse providers. The review analyzes existing and proposed defence mechanisms, spanning cryptographic techniques, fuzzy-logic decision frameworks, AI and ML-driven detection systems, as well as integrated Identity and Access Management (IAM) systems. Analysis of relevant literature reveals a progression from basic foundational encryption systems toward more sophisticated, policy-driven, and collaboration-capable security frameworks. Additionally, the paper identifies significant research gaps in real-world validation, cost optimization, and unified governance models. This research departs from prior work by integrating multiple perspectives rather than limiting its scope to a single area such as security, defence, or cost optimization. It also provides new researchers with comprehensive background information on cloud architecture within a single article.

Facial expression analysis using artificial intelligence (AI) represents an emerging approach for assessing mental health, particularly in neurocognitive disorders. This study encompassed observational investigations that assessed facial expressions in individuals aged 60 years and above. A comprehensive literature search was carried out across PubMed, Scopus, EMBASE, and Web of Science. Risk of bias and study quality were assessed using the QUADAS-2 and CLAIM tools. Descriptive analysis and meta-analysis of proportions were performed using STATA version 19. The pooled effect size (ES) was calculated using a random-effects model (DerSimonian–Laird method), and results were presented with corresponding 95% confidence intervals (CI). Six studies were analyzed, comprising a total of 433 participants aged over 60 years, representing diverse AI applications in the detection of neurocognitive disorders. The disorders evaluated included mild cognitive impairment (MCI) (37.4%), dementia (29.3%), and Alzheimer’s disease (AD) (33.3%). Most studies (83.3%) used video-based facial recordings analyzed through deep learning algorithms and emotion recognition models. The pooled meta-analysis demonstrated that AI-based facial recognition algorithms achieved a high overall detection accuracy in older adults (ES = 0.84; 95% CI: 0.77–0.91), with the best performance observed in Alzheimer’s disease (ES = 0.93; 95% CI: 0.89–0.97). AI-based facial analysis demonstrates high, robust, and non-invasive accuracy for the early and differential detection of neurocognitive disorders, including MCI, dementia-related conditions, and AD, in older adults.

Nowadays, driven by the exponential growth of parameters and training data of AI applications and Large Language Models, a single GPU is no longer sufficient in terms of computing power and storage capacity. Building high-performance multi-GPU systems or a GPU cluster via vertical scaling (scale-up) has thus become an effective approach to break the bottleneck and has further emerged as a key research focus. Given that traditional inter-GPU communication technologies fail to meet the requirement of GPU interconnection in vertical scaling, a variety of high-performance inter-GPU communication protocols tailored for the scale-up domain have been proposed recently. Notably, due to the emerging nature of these demands and technologies, academic research in this field remains scarce, with limited deep participation from the academic community. Inspired by this trend, this article identifies the challenges and requirements of a scale-up network, analyzes the bottlenecks of traditional technologies like PCIe in a scale-up network, and surveys the emerging scale-up targeted technologies, including NVLink, OISA, UALink, SUE, and other X-Links. Then, an in-depth comparison and discussion is conducted, and we express our insights in protocol design and related technologies. We also highlight that existing emerging protocols and technologies still face limitations, with certain technical mechanisms requiring further exploration. Finally, this article presents future research directions and opportunities. As the first review article fully focusing on intra-node GPU interconnection in a scale-up network, this article aims to provide valuable insights and guidance for future research in this emerging field, and we hope to establish a foundation that will inspire and direct subsequent studies.

In 2024, a total of 361 billion emails were sent and received by businesses and consumers each day. Email remains the preferred method of communication for work-related matters, with knowledge workers spending two to five hours a day managing their inboxes. The advent of Large Language Models (LLMs) has introduced new possibilities for personalized email automation, offering context-aware and stylistically adaptive responses. However, achieving effective personalization introduces technical, ethical, and security challenges. This survey presents a systematic review of 32 papers published between 2021 and 2025, identified using the PRISMA methodology across Google Scholar, IEEE Xplore, and the ACM Digital Library. Our analysis reveals that state-of-the-art email assistants integrate RAG and PEFT with feedback-driven refinement. User-centric interfaces and privacy-aware architectures support these assistants. Nevertheless, these advances also expose systems to new risks such as Trojan plugins and adversarial prompt injections. This highlights the importance of integrated security frameworks. This review provides a structured approach to advancing personalized LLM-based email systems, identifying persistent research gaps in adaptive learning, benchmark development, and ethical design. This work is intended to guide researchers and developers who are looking to create secure, efficient, and human-aligned communication assistants.

Mobile devices are frequent targets of malware due to the large volume of sensitive personal, financial, and corporate data they process. Traditional static, dynamic, and hybrid analysis methods are increasingly insufficient against evolving threats. This paper proposes a resilient deep learning framework for Android malware detection, integrating multiple models and a CPU-aware selection algorithm to balance accuracy and efficiency on mobile devices. Two benchmark datasets (i.e., the Android Malware Dataset for Machine Learning and CIC-InvesAndMal2019) were used to evaluate five deep learning models: DNN, CNN, RNN, LSTM, and CNN-LSTM. The results show that CNN-LSTM achieves the highest detection accuracy of 97.4% on CIC-InvesAndMal2019, while CNN delivers strong accuracy of 98.07%, with the lowest CPU usage (5.2%) on the Android Dataset, making it the most practical for on-device deployment. The framework is implemented as an Android application using TensorFlow Lite, providing near-real-time malware detection with an inference time of under 150 ms and memory usage below 50 MB. These findings confirm the effectiveness of deep learning for mobile malware detection and demonstrate the feasibility of deploying resilient detection systems on resource-constrained devices.

Programmable data planes are increasingly essential for enabling In-band Network Telemetry (INT), fine-grained monitoring, and congestion-aware packet processing. Although the P4 language provides a high-level abstraction to describe such behaviors, implementing them efficiently on FPGA-based platforms remains challenging due to hardware constraints and limited compiler support. Building on P4THLS framework, which leverages HLS for FPGA data-plane programmability, this paper extends the approach by introducing support for P4-style stateful objects and a structured metadata propagation mechanism throughout the processing pipeline. These extensions enrich pipeline logic with real-time context and flow-level state, thereby facilitating advanced applications while preserving programmability. The generated codebase remains extensible and customizable, allowing developers to adapt the design to various scenarios. We implement two representative use cases to demonstrate the effectiveness of the approach: an INT-enabled forwarding engine that embeds hop-by-hop telemetry into packets and a congestion-aware switch that dynamically adapts to queue conditions. Evaluation of an AMD Alveo U280 FPGA implementation reveals that incorporating INT support adds roughly 900 LUTs and 1000 Flip-Flops relative to the baseline switch. Furthermore, the proposed meter maintains rate measurement errors below 3% at 700 Mbps and achieves up to a 5× reduction in LUT and 2× reduction in Flip-Flop usage compared to existing FPGA-based stateful designs, substantially expanding the applicability of P4THLS for complex and performance-critical network functions.

NISQ (Noisy Intermediate-Scale Quantum) era constraints, high sensitivity to noise and limited qubit count, impose significant barriers on the usability of QPUs (Quantum Process Units) capabilities. To overcome these challenges, researchers are exploring methods to maximize the utility of existing QPUs despite their limitations. Building upon the idea that the execution of a quantum circuit’s shots does not need to be treated as a singular monolithic unit, we propose a methodological framework, termed shot-wise, which enables the distribution of shots for a single circuit across multiple QPUs. Our framework features customizable policies to adapt to various scenarios. Additionally, it introduces a calibration method to pre-evaluate the accuracy and reliability of each QPU’s output before the actual distribution process and an incremental execution mechanism for dynamically managing the shot allocation and policy updates. Such an approach enables flexible and fine-grained management of the distribution process, taking into account various user-defined constraints and (contrasting) objectives. Demonstration results show that shot-wise distribution consistently and significantly improves the execution performance, with no significant drawbacks and additional qualitative advantages. Overall, the shot-wise methodology improves result stability and often outperforms single QPU runs, offering a robust and flexible approach to managing variability in quantum computing.

This paper proposes an efficient sparse channel estimation method for millimeter wave (mmWave) hybrid multiple-input multiple-output (MIMO) systems. The performance of orthogonal matching pursuit (OMP) and its advanced variants—generalized OMP (GOMP), simultaneous OMP (SOMP), and regularized OMP (ROMP)—is evaluated based on normalized mean square error (NMSE) and computational complexity. A new hybrid GOMP–ROMP algorithm is proposed to achieve faster convergence and lower computational costs while maintaining the desired estimation accuracy. Simulation results reveal that the proposed algorithm reduces NMSE by 0.040823 compared to OMP and attains ROMP’s accuracy with significantly less complexity. For a MIMO system with 32 ×32 configuration, the method offers up to a fourfold reduction in computational complexity compared to OMP, ROMP, and SOMP. These findings highlight the potential of the hybrid algorithm for real-time mmWave massive MIMO applications in fifth-generation (5G) and sixth-generation (6G) systems, where high bandwidth and low latency are essential.

Dynamic and efficient resource allocation is critical for Software-Defined Networking (SDN) enabled sixth-generation (6G) networks to ensure adaptability and optimized utilization of network resources. This paper proposes a reinforcement learning (RL)-based framework that integrates an actor–critic model with a modular SDN interface for fine-grained, queue-level bandwidth scheduling. The framework further incorporates a stochastic traffic generator for training and a virtualized multi-slice platform testbed for a realistic beyond-5G/6G evaluation. Experimental results show that the proposed RL model significantly outperforms a baseline forecasting model: it converges faster, showing notable improvements after 240 training epochs, achieves higher cumulative rewards, and reduces packet drops under dynamic traffic conditions. Moreover, the RL-based scheduling mechanism exhibits improved adaptability to traffic fluctuations, although both approaches face challenges under node outage conditions. These findings confirm that queue-level reinforcement learning enhances responsiveness and reliability in 6G networks, while also highlighting open challenges in fault-tolerant scheduling.