federated learning

FedFAn: Federated Learning for Facial Animation

We present FedFAn: a system for training audio-driven facial animation models across organisational silos via federated learning. AI model providers often serve companies who hold sensitive biometric and proprietary data which is used for model inference and would make valuable training data, but cannot be shared with...

AgroGuard AI: A Trustworthy Adaptive Architecture for Crop Disease Intelligence under Environmental Non-Stationarity

Deep learning systems for crop disease recognition are routinely reported at above 95% accuracy on curated benchmarks, yet their field performance degrades sharply and silently once deployed. The cause is not a modelling deficiency in the conventional sense but a violation of the stationarity assumption on which superv...

Channel Coefficient Estimation in Massive MIMO System using RIS and Federated Learning Techniques

Reconfigurable Intelligent Surfaces (RIS) enhance the spectral efficiency and coverage of massive MIMO systems by establishing programmable reflection channels. However, the passive nature of RIS elements results in high-dimensional cascaded channels that make accurate channel state information (CSI) acquisition challe...

Spectral Efficiency Improvement in THz Massive MIMO System Through Federated Learning Based channel Training

This work proposes an efficient channel training and estimation framework for Terahertz (THz) massive MIMO systems using a Federated Learning (FL)-based approach integrated with Convolutional Neural Networks (CNN). Although THz communication enables ultra-high data rates for next-generation wireless systems, it faces c...

Federated domain generalization for planetary gearbox fault diagnosis: Frequency band energy matrix-guided fusion with prototype regularization

Federated domain generalization (FDG) addresses privacy-preserving fault diagnosis under cross-client domain shift by learning from source clients for unseen target clients. In planetary gearboxes, varying operating conditions can make the same fault exhibit different fault-related spectral characteristics across clien...

IoT-Based Early Detection of Chronic Diseases Using Machine Learning

Chronic diseases, including cardiovascular disease, diabetes, chronic respiratory disease, and cancer, remain major causes of morbidity and premature mortality worldwide.The increasing availability of Internet of Things (IoT) devices, wearable sensors, smart phones, and remote monitoring platforms provides an opportuni...

Project: Privacy-Preserving Federated Learning for Science

This repository is part of a U.S. Department of Energy (DOE) Advanced Scientific Computing Research (ASCR) project focused on developing privacy-preserving federated learning approaches for scientific foundation models. The project aims to advance secure, trustworthy, and sustainable machine learning methodologies for...

From Model Approval to Interface Governance: Federated Learning, Lifecycle Accountability, and AI-Enabled Software as a Medical Device

AI-enabled Software as a Medical Device (AI-SaMD) is increasingly governed through lifecycle instruments such as predetermined change control plans and Good Machine Learning Practice. Federated learning has been advanced as a privacy-preserving alternative to centralized training, but it destabilizes a foundational ass...

Generative AI‐Driven Patient Twins

The convergence of machine learning (ML), artificial intelligence (AI), and edge computing is reshaping the landscape of healthcare decision support systems. Traditionally, healthcare analytics have relied heavily on cloud-based infrastructures, latency, privacy concerns, and dependence on continuous connectivity. Edge...

Dual-LR: Learning shared transformation knowledge through additive low-rank knowledge decomposition for personalized federated learning

SemLRA: Lightweight Robust Aggregation Against Poisoning Attacks in Semantic Federated Learning

Semantic federated learning couples task-oriented semantic communication with distributed training, reducing on-device training burden and end-to-end inference latency. However, poisoning through local data, task labels, or transmitted semantic features can degrade task performance and system reliability. We propose se...

TRACE: Trust Recalibrated and Accountable Federated Learning with Composition Correct Differential Privacy for Zero Trust Consumer IoT Data Sharing