Jul 2026· International Conference Computing Methodologies and Communication· pp. 1081-1086· 0 citations· 19 references
Abstract
The need to train distributed models on cloud and edge devices is of growing concern in terms of security, privacy, and efficiency. Federated learning (FL) is a potential solution to training machine learning models without causing data decentralization, but its implementation in cloud-edge systems creates issues like data heterogeneity, communication overhead, and susceptibility to security attacks. To counteract this, the paper introduces a stronger federated learning architecture that guarantees the security and privacy-friendly model training at cloud and edge layers. The frameworks are new algorithms, which take into consideration the latest encryption algorithms and differential privacy schemes, and optimization of communication protocols to provide better efficiency. This solution creates secure aggregation and a hybrid edge-cloud interaction model, and it reduces the risks of data leakage and unauthorized access to the information during the training process. The results of the experiments show that the improved federated learning framework attains a notable degree of balance concerning the model accuracy, privacy preservation, and communication cost, which is better in security and computational efficiency than the current federated learning frameworks. The present paper adds a powerful framework of safe, privacy-confidential zed, and distributed cloud-edge machine learning as a basis of future developments in the field.
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
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
In the era of edge computing, where data is generated and processed at the network's edge, ensuring privacy and scalability in machine learning models is paramount. Federated Learning (FL) addresses these challenges by allowing multiple edge devices to collaboratively train models without sharing raw data. This paper investigates the implementation of FL in distributed cloud systems, highlighting its role in preserving data privacy and improving scalability. We analyze various FL algorithms, such as Federated Averaging (FedAvg) and Hybrid Federated Dual Coordinate Ascent (HyFDCA), assessing their effectiveness in edge computing contexts. Additionally, we explore techniques like inverse distance aggregation to handle non-IID data distributions and discuss the trade-offs between communication and computation in FL frameworks. Through comprehensive analysis and experimentation, this study provides insights into optimizing FL for edge computing, paving the way for more secure and scalable machine learning applications in distributed cloud environments.
Kenji Sato· International Journal of Art...· 0 citations
Experimental results demonstrate high model accuracy, reduced privacy leakage, lower communication overhead, faster convergence, enhanced scalability, and strong resilience against security attacks, making the proposed framework suitable for next-generation privacy-preserving smart applications.
Seshagiri N· International Journal of Mod...· 0 citations
Federated learning (FL) is a machine learning technique where multiple clients with local data collaborate in training a machine learning model. However, this centralization of sensitive model updates incurs security and privacy issues. A malicious aggregator can launch inference attacks to violate the privacy of clients' private data from local models and explore sensitive knowledge from global model. Trusted Execution Environment (TEE) based schemes offer secure enclaves to protect models privacy during the aggregation procedure. However, TEE technologies incur new functional and security assumptions, where a single vulnerability could undermine the trusted system. Cryptographic schemes such as homomorphic encryption (HE), differential privacy (DP) and secret sharing (SS) cannot achieve the simultaneous requirements of efficiency, accuracy and privacy currently. In this paper, we present DoshFL that avoids expensive cryptographic operations and enables tunable trade-off between privacy and efficiency in federated learning for model asset protection.
Jia-Ming Fang· 2026 3rd World Conference on...· 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