federated learning

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...

Hybrid Distributed Learning With Knowledge Distillation for Resource-Efficient Intrusion Detection in Distributed Networks

A novel AI-driven distributed NIDS that considers the computing capabilities of resource-constrained nodes while enabling efficient learning in distributed environments is proposed and can achieve accuracy comparable to a centralized model while reducing local computational overhead and maintaining stable convergence u...

Twin2Twin: Digital Twin–Driven Orchestration of Federated Learning in Medical IoT Environments

Sixth-generation (6G) networks are expected to integrate intelligence as a native capability, enabling advanced verticals such as digital health (eHealth) supported by large-scale Internet of Medical Things (IoMT) deployments. In this context, Federated Learning (FL) is emerging as a promising paradigm for collaborativ...

HBA: Hijacking-Based Backdoor Attack for Vertical Federated Learning

Vertical federated learning (VFL) is a distributed machine learning paradigm designed for scenarios with vertically partitioned data features, making it highly compatible with Internet of Things (IoT) ecosystems. While promoting collaborative modeling among IoT devices, VFL also introduces new security risks, particula...

FedProIn: Mitigating Client Drift for Learnable Prototypes in Federated Medical Imaging

This work proposes a prototype-based, influence-aware federated learning framework (FedProIn) that uses multiple learnable class prototypes to capture shared semantic structures across heterogeneous clients and introduces feature divergence loss and prototype contrastive loss to mitigate client drift by decomposing it...

RCFL: Recursive Clustered Federated Learning for Distributed Concept Drift

FedJoint: A software architecture for adaptive orchestration in federated learning systems