The rapid expansion of Edge Computing (EC) and Internet of Things devices has introduced significant cybersecurity challenges, necessitating advanced and privacy-preserving attack detection strategies. Traditional cyber-attack detection methods and centralized machine learning solutions face critical limitations in addressing privacy concerns, resource constraints, and the evolving nature of cyber threats in edge environments. Federated Learning (FL) offers a transformative solution by enabling distributed model training across edge devices while preserving data privacy. This systematic literature review investigates FL for cyber-attack detection in EC environments using the PRISMA methodology, analyzing 131 primary studies from 2020–2025 across five major databases. Our contributions include: (1) a comprehensive PRISMA-compliant framework encompassing seven thematic areas with detailed comparative analysis, (2) an in-depth gap analysis with actionable recommendations for privacy-performance trade-offs, scalability, and standardization challenges, and (3) a forward-looking research agenda addressing generative models, collaborative defense, 6G-enabled intelligence, and zero-trust architectures. Unlike existing surveys, this work provides the most comprehensive scope with bibliometric analysis, multi-perspective evaluation, and practical deployment guidelines, serving as a foundational reference for advancing federated cyber-attack detection in edge computing environments.

Smart grids (SG) are the well-known cyber-physical systems responsible for processing, managing, controlling and optimising power energy for multi-scale organisations. Advancements in computing technologies have significantly revolutionised their performance with the integration of the Internet of Things (IoT), artificial intelligence (AI) and cloud technologies. However, such advancements also yield unforeseen vulnerabilities that pose serious threats to the grid management and control. In particular, the rapid advancement of AI and the increasing complexity of adversarial and other cyber attacks have created a highly vulnerable security landscape, despite years of countermeasure development. This study comprehensively reviews the recognition mechanisms utilised against such threats from the perspective of three layers of SG, such as sensing, communication and application layers. Additionally, it maps a wider range of cyber attacks with SG-related threat models and characteristics, along with vulnerable SG components. More importantly, it provides an in-depth comparative analysis of benchmark datasets and related evaluation metrics, including power system metrics, which is unique among other studies. This work also highlights the contemporary limitations and essential recommendations for future integration. Overall, the primary objective of the study is to guide scholars in understanding cutting-edge anomaly recognition techniques employed in SG and provide recommendations for mitigating challenges posed by advanced threats. • Mapping of smart grid-related cyber threat models, characteristics and component vulnerabilities. • Deeper analysis of smart grid-related cyber attack benchmark datasets and assesses the power system evaluation metrics. • Comprehensive analysis of recognition methods while highlighting deep learning based approaches for adversarial and emerging attacks. • Discussion of the contemporary challenges associated with these contexts and key recommendations.

The concept of sustainability, as outlined by the United Nations’ Sustainable Development Goals (SDGs), refers to the ability to meet the needs of the present without compromising the ability of future generations to meet their own needs. This vision is addressed by combining goals concerning the environmental , social , and economic spheres. In this context, Recommender Systems (RS) have emerged as tools that can foster these principles by nudging responsible user behavior and promoting sustainable decision-making. However, the interplay between RS and sustainability is inherently complex since it can be analyzed from two different perspectives: (i) RS for Sustainability , which focuses on how recommendation algorithms can support the achievement of SDGs, and (ii) Sustainability of RS , which focuses on developing recommendation models that inherently adhere to sustainability principles. While the integration of both these perspectives is beneficial and crucial, unfortunately, the current literature has addressed these aspects independently. Accordingly, in this survey, we first provide a comprehensive review of the existing literature on RS that either promotes sustainable behaviors aligned with the SDGs or embeds sustainability principles into their algorithmic design. Next, we identify current gaps and propose key research directions toward an integrated, holistic approach that concurrently addresses both aspects to advance the development of sustainable RS. • This survey distinguishes two key dimensions: Recommender Systems (RS) for Sustainability and Sustainability of RS. • The work reviews existing studies that promote sustainable behaviors or embed sustainability principles in their design. • The work identifies critical gaps in current research and outlines a roadmap for developing sustainable recommender systems.