federated learning

Decentralized Federated Learning with Differential Privacy via Homomorphic Encryption

This paper presents a novel approach to decentralized federated learning that leverages the strengths of differential privacy and homomorphic encryption to achieve robust privacy guarantees. The core idea is to encrypt each participant's local data using homomorphic encryption, allowing a central server to perform comp...

Federated Learning for Privacy-Preserving Data Analysis

This paper explores the application of federated learning (FL) as a novel approach to privacy-preserving data analysis. Traditional data analysis methods often require centralized data collection, raising significant privacy concerns. Federated learning offers a compelling alternative by enabling collaborative model tr...

DEx-FCL: Reproducibility package for adaptive Edge-IoT security management

Reproducibility package for the manuscript "DEx-FCL: Drift- and Explanation-Consistent Federated Continual Learning for Adaptive Edge-IoT Security Management." This release includes the DEx-FCL source code, deterministic federated partitioning utilities, fixed five-seed configurations, CICIoT2023 and Edge-IIoTset bench...

Differential Privacy for Federated Learning with Personalized Noise

Federated learning (FL) offers a promising approach to training machine learning models on decentralized data, preserving data privacy. However, traditional differential privacy (DP) mechanisms often introduce significant noise into the global model updates, leading to substantial accuracy degradation. This paper prese...

Distributed Generative Adversarial Networks with Federated Learning

This paper presents a novel approach to generative modeling by integrating Generative Adversarial Networks (GANs) with Federated Learning (FL). Traditional GAN training suffers from centralized data requirements and privacy concerns. This research addresses these limitations through a distributed GAN architecture speci...

Securing Transactions: A Novel Federated Approach to Credit Card Fraud Detection Using Metaheuristic Optimization Techniques

Credit card transaction fraud has resulted in a massive loss to both consumers and banks in recent years.As a result, this research proposes an optimized framework for fraud detection.This framework will allow banks to construct fraud detection models using training data stored in their internal database.With this appr...

Integrating Particle Swarm Dynamics into Federated Learning: A Promising Hybrid to Counter Client Drift under Non-IID Conditions

Federated learning (FL) enables distributed model training without centralizing raw data, making it attractive for privacy-sensitive Internet of Things (IoT) environments.However, conventional FL algorithms suffer from slow convergence and degraded performance under statistically heterogeneous (non-IID) data distributi...

Execution Governance 5.0 Research Architecture: From Governed Effect Fabrics to Governed Effect Regimes

Execution Governance 5.0 (EG5) Research Architecture v0.1.4 proposes a candidate major-version research direction extending Execution Governance from authority-preserving Governed Effect Fabrics to the time-evolving Governed Effect Regime that determines how such fabrics, authority roots, policies, comparators, composi...

Execution Governance 5.0 Research Architecture: From Governed Effect Fabrics to Governed Effect Regimes

Execution Governance 5.0 (EG5) Research Architecture v0.1.4 proposes a candidate major-version research direction extending Execution Governance from authority-preserving Governed Effect Fabrics to the time-evolving Governed Effect Regime that determines how such fabrics, authority roots, policies, comparators, composi...

Distributed Graph Learning via Federated Bayesian Networks

This paper presents a novel approach to distributed graph learning utilizing Federated Bayesian Networks (FBNs). The core challenge in training large graph neural networks (GNNs) lies in the substantial computational resources required, often necessitating centralized training environments. Federated Bayesian Networks...

DAP-FedGL: A decentralized adaptive protection framework for client-level privacy in federated graph learning

End-to-End Federated Intelligence for Secure and Standardized Digital Twin Orchestration in 6G Smart Cities

Digital twins (DTs) are rapidly emerging as foundational enablers of 6G smart cities, offering real time monitoring, predictive analytics, and autonomous control across transportation, energy, healthcare, and industrial domains. Large scale DT adoption faces critical barriers including cybersecurity vulnerabilities, pr...