federated learning

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...

Research on Privacy Protection of Digital Twin Intelligence Based on Big Data in 5G System Security

Digital twin (DT) technology is becoming a foundational component of next-generation 5G and 6G networks, enabling real-time monitoring, predictive analytics, and collaborative research. However, integrating DTs with big data and AI introduces significant privacy, security, and governance challenges. This article presen...

Constructing a Personalized Learning Path Recommendation Model Based on Federated Learning

A personalized learning path recommendation model based on federated learning that effectively balances global generalization and local adaptation while maintaining privacy protection is constructed, providing a feasible solution for secure and intelligent recommendation in distributed learning environments and offerin...

Efficient Backdoor Mitigation in Federated Learning With Contrastive Loss

The rapid adoption of Internet of Things (IoT) devices has accelerated the need for privacy-preserving machine learning techniques, such as federated learning (FL). However, the decentralized and collaborative nature of FL makes it vulnerable to backdoor attacks, where adversaries locally update their malicious models...

A Blockchain-Based Federated Learning Approach for Electricity Theft Detection Through Dual-Verification

Malicious clients participating in data collection and interaction may launch attacks such as model and data poisoning to degrade the performance of the global model and conceal their electricity theft behaviors. Although existing studies have introduced blockchain technology to achieve decentralization, they still suf...

A Spatiotemporal Coupling-Based Clustered Federated Learning Scheme for Low Latency Digital Twin Within Heterogeneous IIoT

Data-driven deep learning (DL) techniques have increasingly been employed to construct digital twin (DT) models for the intelligent industrial Internet of Things (IIoT) systems. Within DTs, federated learning (FL) offers a decentralized framework that enables distributed entities to collaboratively update global models...

CSFL: Communication-Efficient Semi-Asynchronous Federated Learning Method in Resource-Constrained Edge Computing

Federated learning (FL) is a distributed machine learning (ML) paradigm that has been widely used to train ML models on massive amounts of data in edge computing (EC) environments. However, FL faces significant challenges from device heterogeneity, edge dynamics, and limited communication resources. To address these ch...