federated learning

##基于多智能体学习的机器人群体协同控制

This paper presents a novel approach to robot swarm coordination control utilizing Multi-Agent Reinforcement Learning (MARL). The core claim is to design a robust and adaptive robot swarm control system by leveraging the learning capabilities inherent in MARL. The proposed system employs a federated reinforcement learn...

Closing the Semantic-Edge Gap: Tiny Language Models for 6G Wireless Intelligence

A two-axis taxonomy connecting six compression families to five semantic communication architectures is synthesized with a quantitative meta-analysis of the model-size-versus-semantic-fidelity Pareto frontier, synthesized with a quantitative meta-analysis of the model-size-versus-semantic-fidelity Pareto frontier.

Towards Global Federated Genome-Wide Association Meta-Analysis Using GA4GH TES

In a five-site simulation, the federated meta-analysis reproduces the association signal expected from a pooled analysis without centralizing any genotype data, showing that standards-based task execution and federated learning enables a practical privacy-preserving infrastructure for international GWAS meta-analysis.

Federated Real-Time Monitoring Method for Industrial IoT Devices and Its Application in Wind Farm Cluster

Driven by industrial Internet of Things (IIoT) technologies, wind farms are evolving into highly digitalized and interconnected energy systems. With the accelerating expansion of renewable energy and the large-scale deployment of wind farm clusters, the demand for collaborative real-time monitoring across multiple farm...

AI-Driven energy forecasting in smart buildings: A federated deep learning framework for Edge-Fog systems

AFcl: Asynchronous federated continual learning with mobile devices across edges

Fast Federated Learning via Imprecise Correction in Unreliable Wireless Networks

Due to data heterogeneity and parameter transmission failure, federated learning (FL) usually suffers performance degradation in wireless networks. To address this issue, a novel federated Learning via reusing historical Information (FedRe) is proposed for convergence acceleration and performance enhancement without an...

AI-Standardized Secure Digital Twins for Smart Home Ecosystems

Digital Twins (DTs) are emerging as foundational components of next-generation communication systems, offering real-time synchronization, predictive modeling, and autonomous control across consumer electronics. However, their integration with AI/ML, edge–cloud infrastructures, and 5G/6G networks significantly enlarges...

Expert-wise federated learning with sparse routing for distributed traffic prediction

DPArena: A platform for reproducible experiments on data poisoning in federated learning

EDAF: An Enhanced Dual-Alignment Framework for Robust Federated Learning in Heterogeneous IoT Environments

Federated learning (FL) in real-world deployments is fundamentally challenged by heterogeneous environments, where clients differ in both model architectures and computational capacities (system heterogeneity), as well as in local data distributions (statistical heterogeneity due to nonindependent and nonidentically di...

Long-Term Optimal Incentives for Differential-Privacy Federated Learning: A Multi-Stage Game Approach

Differential-privacy federated learning (DP-FL) has emerged as a promising paradigm capable of mitigating the inherent threat of traditional FL architectures that are vulnerable to inferential attacks due to the frequent exchange and updating of model parameters. However, existing DP-FL frameworks often assume that the...