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

FedGRAIL: Trustworthy and Efficient Federated Class-Incremental Learning for Long-Tailed IoT Edge Intelligence

Digital Twin-Assisted Intent-Driven Autonomous Virtual Network Function Orchestration Using Federated Intelligence for Resilient 6G Networks

The 6G networks require autonomous and smart management of resources to support ultra-dense devices, dynamic traffic and low-latency services. The classic methods of Network Function Virtualization (NFV) orchestration utilize primarily the use of either a static or heuristics algorithm, which constrains their capacity to adjust to the dynamically evolving network conditions. Additionally, the current digital twin and distributed learning systems are characterized by a high level of synchronization overhead and poor automation abilities. To cope with these issues, this paper suggests a Federated Intelligence-based Digital Twin-Assisted Intent-Directed Autonomous Virtual Network Function (VNF) Orchestration. The proposed methodology combines the idea of digital twins to represent a real-time network with the idea of federated learning so that one can train models using a set of distributed edge nodes and maintain data privacy. In Python, the machine learning model is applied to interpret the network traffic data and forecast the demands of resources to be efficient in the orchestration of VNF. The experimental findings indicate that the suggested framework with such a high prediction precision of about 98, a higher usage of resources and a lower latency rate. The paper concluded that incorporating digital twins and federated intelligence could be useful in future 6G networks management to achieve high degrees of automation, scalability, and resilience.

Toward Federated Cognitive Digital Twins over the Edge-to-Cloud Continuum

Digital Twins (DTs) are increasingly adopted to monitor, analyze, and optimize Cyber-Physical Systems (CPSs) through continuous interaction between physical assets and their digital counterparts. However, current DT architectures often rely on centralized and monolithic designs, leading to scalability, latency, and resilience issues in distributed environment such as smart cities. Moreover, they provide limited support for semantic integration and high-level reasoning, reducing the effectiveness of DT-based decision-making. Recent studies on Federated Digital Twins (FDTs) have addressed scalability by decomposing complex systems into interacting twins, but they still largely centralize intelligence in cloud components. In parallel, Cognitive Digital Twins (CDTs) enhance DTs with semantic reasoning, explainability, and AI-driven decision support, yet they are typically difficult to integrate into distributed architectures. This paper proposes a Federated Cognitive Digital Twin (FCDT) architecture that combines federation and cognition within a unified approach. The architecture distributes intelligence across the edge-to-cloud continuum through local twins, which provide real-time monitoring and lightweight cognitive capabilities, and global twins, which perform system-level reasoning, simulation, and coordination. By integrating distributed autonomy with cognitive reasoning, the proposed approach improves scalability, responsiveness, and decision-making in complex distributed CPSs

Efficient Federated LLM Framework for Intelligent IoMT Management

6G-Integrated Federated Learning for Trustworthy and Scalable Edge-IoT Collaboration

The intersection of sixth-generation (6G) communication networks, edge computing, and federated learning offers a novel occasion regarding empowering trustful and scalable collaboration throughout distributed Internet of Things (IoT) ecosystems. Conventional centralized machine learning technology is plagued by severe constraints in IoT scenes, such as privacy issues, network congestion, and network bottlenecks. The paper has presented a new 6G-integrated federated learning system which builds on the native intelligence of 6G networks to support secure, efficient, and scalable cooperative learning among heterogeneous edge-IoT devices. The suggested architecture combines terahertz frequencies to synchronize model communication in real-time at ultra-low latency, reconfigurable intelligent surfaces to improve the quality of communication, and network slicing to provide differentiated quality-of-service assurances. Another new hierarchical federated learning system integrates intra-edge aggregation and inter-edge cooperation that can reduce communication overhead by 85-percent and achieve the same accuracy in models. This framework integrates blockchain-based trust management involving zero-knowledge proofs of verifiable model update, to provide integrity and accountability without impacting on privacy. Experimental analysis of massive scale edge-IoT applications has revealed that the suggested scheme attains 97.2% model precision and lowers communication expenses by 87 percent and convergence rate by 3.4 times that of traditional federated learning techniques. The framework has high-uniform performance in adversarial environments where 99.6 percent of malicious model updates are identified with a small false positive. The results define the 6G-integrated federated learning as a framework of reliable and scalable edge-IoT cooperation.

HongAvg: Hierarchical On-Demand Cognitive Split Federated Learning for Twin Agent Orchestration

Fusing digital twins (DTs) with world models (WMs) promises a leap from reactive monitoring to proactive control. However, deploying high-fidelity WMs faces a fundamental cognitive gap: the immense computational demand of foundation models conflicts with the resource constraints of 6G edge nodes, while privacy regulations hinder the centralization of raw sensory data required for training. To bridge this gap, this paper presents HongAvg, a hierarchical on-demand cognitive split federated learning framework designed as cognitive infrastructure for next-generation DTs. By establishing a national-basin-edge three-tier architecture, HongAvg introduces foundation models to resource-constrained edges via split computing, offloading heavy cognitive reasoning while preserving data privacy. We propose a dual-stream semantic consistency mechanism to align edge interactions with the foundation model's cognition, ensuring that distributed cognitive primitives serve as valid inputs for global state estimation. Validated on a heterogeneous benchmark, HongAvg serves as a prototype for industrial cognitive computing, improving accuracy in visual monitoring by up to 8.9% and reducing edge memory usage by approximately 75%. This work provides the scalable architectural prerequisite necessary for evolving static DTs into proactive WM-driven multi-agent orchestrated intelligent systems.