The security of smart grids is seriously threatened by false data injection (FDI) attacks. Falsified data is maliciously injected into the grid's measurement and control systems as part of these attacks, which might seriously disrupt the power supply and jeopardize system integrity. In the context of smart grids, it is also imperative to address the issue of consumer privacy and the protection of their sensitive data. The main objective of this work is to provide a collaborative framework based on federated learning to detect various FDI dangers while protecting SG's resources and privacy. We have implemented several technologies that provide a good solution in order to accomplish this goal. Using a dataset designed to replicate attacks on the power system environment, we used federated learning to locally train models using the data stored on the sensors. The best model should then be chosen by comparing the outcomes. These outcomes demonstrate the potential of our framework, which has used mixed models to repel attacks, short-circuit faults, and maintain lines with a 98% accuracy rate during the federated learning phase.

The exponential growth of Internet of Things (IoT) devices integrated with fifth-generation (5G) wireless networks has created unprecedented opportunities for ultra-low-latency applications while introducing complex security vulnerabilities and computational challenges. This paper presents a comprehensive framework for deploying adaptive lightweight Convolutional Neural Networks (CNNs) in 5G-enabled IoT environments to address intrusion detection, intelligent traffic classification, and dynamic resource optimization. We propose a novel multi-objective optimization approach that integrates Adaptive Depthwise Separable Convolutions (ADSC), Dynamic Quantization-Aware Training (DQAT), and Real time Pruning Strategy (RPS) specifically designed for 5G network slicing architectures. Our methodology incorporates federated learning principles, edge-cloud collaboration, and context-aware adaptation mechanisms. Comprehensive evaluation on multiple datasets, including NF-ToN-IoT-v2, NSL-KDD, and CICIDS-2017, demonstrates superior performance with 97.8% accuracy in multi-class attack detection, 76% reduction in computational overhead, 71% decrease in energy consumption, and 42% improvement in network throughput. The framework achieves inference times under 8.5ms on edge devices while maintaining robust security postures across heterogeneous IoT deployments. Statistical significance testing and large-scale ablation studies verify the effectiveness of each of the suggested elements.

This bibliometric survey examines 200 highly cited publications spanning from 2018 to 2025, retrieved from the Scopus database using search terms targeting federated learning applications in healthcare, medical imaging, and clinical diagnostics. The analysis reveals a rapidly maturing interdisciplinary field at the convergence of distributed machine learning, privacy-preserving technologies, and biomedical informatics. Key findings indicate that federated learning has emerged as a transformative paradigm for collaborative AI model training across healthcare institutions without compromising patient data privacy. The United States, China, the United Kingdom, Germany, and India emerge as the most productive nations, with extensive international collaboration networks reflecting the global need for privacy-compliant healthcare AI. Thematic clustering identifies five major research domains: (1) federated learning architectures and algorithms for healthcare, (2) privacy-preserving techniques including differential privacy, homomorphic encryption, and blockchain integration, (3) medical imaging applications (radiology, pathology, dermatology, ophthalmology), (4) electronic health records and predictive analytics, and (5) wearable devices and remote patient monitoring. Emerging trends include the integration of federated learning with explainable AI for clinical interpretability, the convergence of federated learning with digital twins and the healthcare metaverse, and the development of personalized federated learning frameworks for handling non-IID medical data. This survey provides a comprehensive mapping of the intellectual landscape, identifies persistent challenges including data heterogeneity, communication efficiency, and security vulnerabilities, and proposes future directions for privacy-preserving healthcare AI.

At the edge of the network, the exponential increase of data produced along with the growing concerns over data privacy coming from regulations and society have all together triggered the rise of Federated Learning (FL) as the main approach in distributed machine learning (DML). Fed learning allows the model training to be performed on decentralized devices or data silos even without the raw data being transferred. Hence, FL is completely in line with the objectives of the privacy-preserving techniques. In this paper, we carry out a scientometric analysis on the 200 most cited papers, which are the first 200 papers at the intersection of "Distributed Machine Learning," "Federated Learning," and "Privacy-Preserving" published between 2020 and 2024, and the Scopus database is where they are indexed. The literature of publication trends, prominent authors and works, the thematic clusters, and research fronts that are changing are all systematically examined in this study; hence, the intellectual landscape of this fast developing field is mapped out. Our findings point to the existence of certain streams of research such as the algorithms with differential privacy being the mainstay, secure aggregation methods through the use of homomorphic encryption and multi-party computation, blockchain-based FL systems which ensure security and trust, and resource-efficient FL that supports IoT and edge computing. The results also show an area that is nearly enjoying a complete transformation as a result of the overpowering need to address the triad of model quality, data protection, and system efficiency. The review not only encourages researchers, and practitioners but also helps the policymakers by providing the current trend to which the key challenges can be identified and the future directions in privacy-preserving distributed intelligence anticipated.