federated learning

AERF: Adaptive Evidence-Assured Predictive-Maintenance Framework

AERF is a modular research framework for governed evaluation of predictive-maintenance models across classification, regression, remaining-useful-life, and physical-edge execution routes. The capsule provides the base framework, campaign executor, model adapters, dataset registry, physical-edge validation utilities, re...

Hyperdimensional Computing for Secure Federated Learning

Federated learning (FL) presents a promising paradigm for training machine learning models across decentralized devices while preserving data privacy. However, the inherent vulnerability of FL to adversarial attacks poses a significant threat to its security and reliability. This paper proposes a novel approach to secu...

Secure and scalable client pre-filtering and authentication framework for federated learning systems

Abstract A federated learning system, which emphasises decentralisation, allows cooperative model training to ensure the privacy and security of the data. Client authentication is an important factor in a decentralised system to prevent unauthorised access and unauthorised users from adding exceptions or irrelevant fac...

Privacy-Preserving Federated Learning for Biophysics Core Life in Uganda: Robustness Under Distribution Shift and Sparse Labels

Laboratories providing biophysics core services in Uganda hold fluorescence images, electrophysiological traces and macromolecular characterisation records that cannot be pooled in a single server for legal, ethical and infrastructural reasons, yet models trained in one site routinely lose accuracy when applied in anot...

Towards secure distributed medical diagnosis: a blockchain-driven MCAU-Net and MAA-Bi-LSTM framework

Lung cancer is considered as the deadliest type of cancer, a good diagnosis as well as effective treatment is based on early detection. By utilizing Deep Learning (DL) techniques, the medical practitioner’s burden is reduced since these models can automate the diagnosis and disease classification. However, these models...

##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...