Cross-Cloud Data Privacy Enhancement: Optimizing Collaborative AI Systems through Federated Learning and LLMs

Federated Predictive Learning with Privacy-Aware Model Aggregation for Distributed Analytics

This study proposes a Federated Predictive Learning with Privacy-Aware Model Aggregation (FPL-PAMA) framework, suitable for applications including healthcare, IoT, smart manufacturing, transportation, and financial fraud detection, providing a secure and scalable solution for next-generation distributed intelligent systems.

A review of federated learning: architectures, challenges, and targeted solutions

A review of federated learning through a structured taxonomy that covers its core architectural paradigms, major learning types, model training approaches, and aggregation mechanisms, and analyzes the principal challenges confronting FL, including privacy and security risks, statistical and system heterogeneity, communication constraints, and global model divergence.

Resilience in Federated Learning-Based IoMT Applications

Internet of Medical Things (IoMT) applications require collaborative learning across healthcare institutions while ensuring patient data privacy. Traditional centralized learning approaches require sharing sensitive medical records, increasing privacy and security risks. Federated Learning (FL) enables distributed model training by exchanging model parameters instead of raw data, but its performance is affected by client dropouts and communication failures. This paper proposes a Resilience-Enhanced Federated Learning Framework for IoMT applications that improves the reliability of collaborative learning under unstable network conditions. The framework incorporates Federated Averaging (FedAvg), resilient aggregation using historical model updates with staleness decay, and quantized model updates to reduce communication overhead. The proposed model was evaluated using a heart disease dataset distributed across multiple healthcare clients. Experimental results demonstrate that the framework achieves 95.72% accuracy while maintaining stable model convergence during client failures. The proposed approach provides a secure, privacypreserving, and fault-tolerant solution for distributed healthcare applications.

Scalable Data Engineering Architectures for Federated Learning in Decentralized Cloud Environments

The growing adoption of Federated Learning (FL) is reshaping the way machine learning models are trained across distributed, privacy-sensitive datasets. However, the scalable and efficient orchestration of data engineering pipelines in decentralized cloud environments remains a significant challenge. This paper presents a comprehensive architectural framework for scalable data engineering tailored for FL in heterogeneous and resource-constrained environments. By integrating modern distributed computing paradigms, such as Kubernetes-based orchestration, edge-aware data preprocessing, and secure federated communication, we propose a modular architecture that addresses data heterogeneity, scalability, and compliance. A case study in a healthcare IoT scenario validates the performance and flexibility of the proposed system. Our work serves as a blueprint for deploying robust FL systems in real-world decentralized cloud ecosystems.

A Novel Framework for Differential Privacy based Federated Continual Learning for Dynamic Medical Data Analysis

Federated MLOps: Secure CI/CD for Distributed Model Training and Deployment

This paper proposes Federated MLOps, a framework that combines CI/CD principles with federated model training to enable secure, automated, and efficient deployment of distributed ML models.