2026· International Journal of Applied Data Science & Modern Computing· 0 citations
TL;DR
An in-depth analysis of federated learning methods and paying special attention to the issue of privacy is provided, which examines new developments, concerns and tradeoffs connected with privacy, effectiveness of communication, model noise, and scalability of systems.
Abstract
The fast proliferation of data-driven applications and intelligent systems has raised problem awareness as far as the concerns regarding data privacy, data security, and regulatory compliance are concerned to a very high level. The conventional centralized machine learning models demand the coalescence of raw data situated in disseminated sources, which is a grave threat of information leaks, unauthorized data accessibility, and breaking a privacy policy like GDPR and HIPAA. Federated Learning (FL) has become a hopeful decentralized learning framework so as to facilitate joint model training among various customers without relocating crude data to a central point. Rather, model updates of the local models are shared and aggregated, hence retaining locality of data and improving privacy. This paper will provide an in-depth analysis of federated learning methods and pay special attention to the issue of privacy. Its research paper investigates the very principles of federated learning, architecture designs, communication scheme, and ways of aggregation. The diverse privacy-enhancing schemes are secure aggregation, differential privacy, homomorphic encryption, trusted execution environments, and are critically analyzed. Moreover, this article examines new developments, concerns and tradeoffs connected with privacy, effectiveness of communication, model noise, and scalability of systems. There is a comparative analysis of federated learning methods presented in organized tabular form and mathematical equations. The existing studies are discussed with their experimental results in order to outline the effectiveness of federated learning to maintain the privacy and achieve the acceptable model accuracy. Lastly, the research issues and future paths are outlined in order to direct the further development of the privacy-preserving federated learning systems.
The research findings suggest that improved federated learning can achieve an optimal predictive performance, privacy protection, and secure collaborative learning, which makes it a viable method for next-generation distributed AI systems.
Sheetal Bawane, Leeladhar Chourasiya, S. Jain et al.· International journal of com...· 0 citations
Deep learning is becoming popular in cloud applications and serves to provide intelligent services; data aggregation in a central location makes sensitive information vulnerable to privacy breaches, regulatory infractions, and adversarial manipulation. All modern privacy mechanisms offer partial protection and frequently lack accuracy, scalability, or practicality in their operations. To overcome these limitations, a federated deep learning model is formulated so that secure joint learning can occur without transferring raw data across the domains of ownership. The framework incorporates training that is decentralized, training that uses differential privacy, training that uses secure aggregation, training that uses encrypted communication, and training that uses trust-based anomaly defense to defend against leakage, poisoning, and inference attacks. It also supports heterogeneous and highly non-IID datasets using adaptive coordination and stability-relevant participation regulation and meets emerging data protection requirements. The methods of resource-conscious orchestration and the optimization of communication eliminate overhead without obstructing the effectiveness of learning. The paradigm has therefore formed a privacy-by-design intelligent cloud ecosystem which ensures confidentiality, maintains performance, enhances robustness, and ensures responsible AI implementation in privacy-related sectors of healthcare, finance, governance, and smart infrastructure.
Sribidhya Mohanty, Pallavi Gupta, Anil Pratap Singh et al.· 2026 International Conferenc...· 0 citations
It was concluded that strategies such as the careful selection of differential privacy parameters and training settings, along with the use of larger datasets, can improve the efficiency of FL and demonstrate that privacy-preserving and high-performance artificial intelligence systems can be securely applied in sensitive domains such as healthcare and finance.
Cagdas Karatas, Hibanur Karadogan, A. Ertug et al.· 0 citations
The use of cloud-based web systems has increased the issue of data privacy, compliance on regulations and secure control of distributed enterprise information. The traditional centralized machine learning models mandate aggregation of data in one server which makes it more dangerous to expose sensitive information. To overcome these limitations, model combines TF-IDF-based features extractions with a deep neural network classifier trained under a federated learning system, and no longer needs raw this paper suggests a Hybrid Federated Privacy-Aware Deep Neural Network (HFPA-DNN) to learn the features of privacy-controllable data on the cloud. The data sharing among distributed cloud nodes is proposed. The model uses the Adam optimization algorithm, binary cross-entropy loss and federated averaging (FedAvg) to aggregate global models. A non-compulsory differentiation privacy system is involved in order to increase resistance to inference and reconstruction attacks. The HFPA-DNN performance is compared to the traditional machine learning baselines, which are Logistic Regression, Support Vector Machines, Random Forest, and centralized deep neural networks. As experimental findings indicate, the suggested strategy can attain a better accuracy, F1-score, and ROC-AUC, and a significant decrease in the risks of privacy exposure and preserving in scalable training performance. Through training curves, confusion matrices, ROC and precision- recall analysis, ablation studies and statistical comparison, the strength and use of the framework in ensuring data governance in the cloud in a secure setting is always verified. The results demonstrate that HFPA-DNN is an efficient trade-off to guarantee predictive performance, low computational cost, and privacy in distributed web system.
Divya sai Jaladi, Ashok Mallempati, Dr. B. Jegajothi· 2026 4th International Confe...· 0 citations
FedE, a multi-precision, multi-source, heterogeneous privacy-preserving federated learning training method based on functional encryption that enhances numerical adaptation during ciphertext computation and prevents model parameter updates from easily compromising privacy in cross-institutional federated learning.
Weijia Liu, Junwen Deng, Hao Li et al.· Computers, Materials & C...· 0 citations
The rapid growth of machine learning (ML) technologies has raised concerns about the privacy and security of sensitive data used in training models. Privacy-preserving techniques such as federated learning, homomorphic encryption, and differential privacy are emerging solutions to protect data in ML applications. However, these techniques often face challenges in terms of scalability, performance, and compliance with data privacy regulations. Sovereign Cloud environments, characterized by strict data governance and jurisdictional controls, offer a potential solution for addressing these challenges. This paper presents a novel framework for integrating privacy-preserving ML techniques within Sovereign Cloud infrastructures. By combining cutting-edge cryptographic approaches with the data sovereignty features of Sovereign Clouds, our framework ensures data privacy, legal compliance, and efficient machine learning at scale. We discuss key challenges in data privacy, scalability, and legal compliance, and propose a set of best practices for deploying privacy-preserving ML in these environments. Additionally, we evaluate the proposed framework through case studies, demonstrating its potential in sectors such as healthcare and finance. The results show that our framework provides a balanced approach to privacy, scalability, and performance, contributing to the future of secure and responsible ML deployment.
Ahmed Hassan· International Journal of Dat...· 0 citations
