Aug 2026· International Conference on Machine Vision and Deep Learning· Vol 14326, pp. 143261M - 143261M-9· 0 citations· 15 references
Engineering
TL;DR
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.
Abstract
Computing systems managing large-scale, heterogeneous data are increasingly vulnerable to privacy breaches and adversarial attacks. This study presents a federated deep learning framework that systematically integrates adaptive privacy noise mechanisms and trust-weighted aggregation within a distributed architecture. The proposed method ensures the protection of sensitive data during collaborative analysis, even in highly dynamic environments, through precise differential privacy control and advanced neural network models. It is capable of real-time adjustments to privacy budgets and aggregation strategies to reduce the likelihood of information leakage while also lowering the risk for each node. Through comprehensive experiments integrating IoT sensor streams and medical transaction data, it has been demonstrated that the system consistently achieves high prediction accuracy while significantly reducing the success rate of participant inference and reconstruction attacks. Quantitative analysis confirms the strong robustness and cross-domain deployment scalability of this solution. Direct comparisons with state-of-the-art technologies show that privacy enhances resilience and significantly reduces utility loss. Providing feasible technical guidelines for secure and compliant big data analysis, it has verified the effectiveness of integrated privacy-preserving deep learning in protecting critical information infrastructure.
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
This study addresses the challenges of privacy leakage and data silos in multi-source heterogeneous data interaction within smart grids by designing a federated multi-source data fusion architecture that combines adaptive local differential privacy with feature space alignment. This architecture utilizes Hessian matrix trace perception to adaptively adjust the local noise budget and introduces a dynamic aggregation selection mechanism based on the maximum mean difference, reconciling the conflict between differential privacy perturbations and feature manifold losses. Experimental results show that, while ensuring strict differential privacy boundaries, the system improves test accuracy by 7.45%, achieves a model inference speed of 45 FPS, and reduces communication resource overhead by 36.5%. Even under extreme conditions such as nonindependent identically distributed skew and 15% Byzantine poisoning attacks, it maintains a 98.40% attack interception rate and robust generalization fusion performance, providing a feasible system solution for building a highly reliable and resilient situational awareness and control foundation for the distribution IoT.
Jiaying Li, Can Pei· International Conference on...· 0 citations
Today's digital age has made privacy and data protection a major concern-generally, with the kind of technologies that are turning things around and bringing everything to the cloud. FL will most likely provide a solution to the distance and make things clear in collaboration without exposing raw information from a consortium to boost its privacy. However, existing FL solutions include such challenges as increased overhead communication, risk in leaking data, and even the inefficiency of secure aggregation.To mitigate these constraints, this research proposes the Autoencoder-Based Federated Learning framework by integrating prevailing techniques such as differential privacy and homomorphic encryption that safeguard both the security and efficiency of the model. This method does not only steal model ideas for autoencoders to compress before sciences transmission but hugely reduces the transmission bandwidth and possibly minimizes gradient leakage. However, adaptive normalization is used to handle institutional heterogeneity to maintain better performance for the model. Conclusion of experimentation indicated that this framework could significantly reduce communication overhead while retaining high federated learning accuracy and even better security. Further, the trust-based client evaluation mechanism is presented to detect malicious behavior and improve reliability regarding federated aggregation. The experiment showed that Autoencoder Based Federated Learning was a scalable, secure, and privacy-efficient solution to applications tailored for healthcare, finance, and other sensitive data environments.
Guman Singh Chauhan, venkata Surya Teja Gollapalli, Kannan Srinivasan et al.· Journal of Science & Technol...· 0 citations
The results demonstrate that federated learning is a scalable and effective method that can achieve privacy compliance in e-commerce analytics within data-restricted environments, and it lays a solid foundation for secure distributed business intelligence.
Jing Hao· International Conference on...· 0 citations
The deepening cyber-physical integration of smart grids has expanded the attack surface of power networks, while centralised intrusion detection schemes struggle with data silos, privacy exposure, and prohibitive communication costs across geographically dispersed substations. This paper proposes a federated deep learning framework that addresses these constraints jointly. A three-tier cloud-edge-terminal architecture confines raw measurements to local devices and exchanges only model parameters across tiers. At each edge node, a hybrid CNN-BiLSTM detector trained under focal loss captures both spatial protocol motifs and temporal attack signatures, including stealthy false data injection and APT traces. Privacy is preserved through a layer-selective mechanism that combines Paillier homomorphic encryption on sensitive gradient slices with calibrated differential privacy on the residual components, pushing the privacy-utility frontier outward without saturating cryptographic cost. An adaptive aggregation rule weights client updates by data quality, drift severity, and marginal validation contribution, mitigating the convergence pathologies that vanilla FedAvg exhibits under sharp non-IID partitioning. Experiments on NSL-KDD, CICIDS2017, and an ICS power-system corpus show that the proposed scheme recovers within 0.7 F1 points of the centralised upper bound, suppresses membership inference advantage to below 0.08, holds detection quality against up to 20% Byzantine clients, and converges in roughly half the rounds required by FedAvg. The framework offers a deployable path toward collaborative intrusion detection across regional grid operators without compromising data sovereignty.
Lin Chen, Zhuo Tang, Yiwei Yang et al.· Scientific Reports· 0 citations