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.
Abstract
This study explores the integration of homomorphic encryption and differential privacy techniques to enhance data privacy and security in Federated Learning (FL) systems. FL allows data to remain on local devices, eliminating the need for centralized data collection; however, sensitive information may still be leaked during model updates. To address this issue, homomorphic encryption enables computations on encrypted data, while differential privacy prevents the extraction of individual information through statistical techniques applied to model outputs. The proposed architecture was tested on the Framingham, Pima Indians Diabetes, and Bank Marketing datasets, revealing that enhanced privacy can be achieved without significantly compromising model accuracy. Furthermore, the impact of data heterogeneity among clients on model performance was analyzed, and 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. The findings demonstrate that privacy-preserving and high-performance artificial intelligence systems can be securely applied in sensitive domains such as healthcare and finance.
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.
Aarav Mehta· International Journal of App...· 0 citations
Federated learning (FL) enables collaborative model training across multiple clients in a privacy-preserving manner. However, the employment of homomorphic encryption algorithms might lead to high computational cost while the application of differential privacy (DP) methods would sacrifice model performance. To establish efficient and secure FL system as well as maintaining competitive performance, we introduce a DP-enabled cascaded filter with novel model-aggregation mechanism. Taking the model parameters of one client for example, dimensions with large absolute values and significant variations are selected by the cascaded filter and regarded as important dimensions. After that, random noise are added to these important dimensions for data security. Finally, the model parameters will be sent to the central server for aggregation. Unlike traditional DP-based approaches, our method considers each dimension’s informational importance, i.e., both absolute value and its variation. We theoretically prove the convergence of our method and verify the effectiveness on different scenarios including four datasets. The experimental results suggest that our method outperforms the other ones from literature under both IID and non-IID conditions while achieving high level of efficiency and privacy protection performance.
Zhiqiang Chen, Yuchen Jiang, Ray Y. Zhong et al.· IEEE Transactions on Informa...· 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
Federated learning (FL) offers a decentralized approach to machine learning that preserves data privacy by training models locally across distributed devices. However, the feature engineering process—an essential step in improving model performance—often remains centralized or privacy-invasive, risking sensitive data exposure. This paper proposes a comprehensive framework for privacy-preserving feature engineering (PPFE) within federated learning analytics. We explore techniques such as homomorphic encryption, differential privacy, and secure multi-party computation to enable robust, privacy-safe feature selection, transformation, and extraction across clients. Our framework includes both vertical and horizontal FL settings and evaluates the trade-offs between privacy, utility, and communication overhead. Experimental results on real-world datasets demonstrate that our PPFE methods can significantly improve model performance without compromising data privacy. This work contributes towards building a more secure and efficient FL pipeline that ensures end-to-end data confidentiality.
Yuki Nakamura, Olivia Martin· International Journal of Dat...· 0 citations
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
A federated deep learning framework that systematically integrates adaptive privacy noise mechanisms and trust-weighted aggregation within a distributed architecture that ensures the protection of sensitive data during collaborative analysis through precise differential privacy control and advanced neural network models is presented.