federated learning

A hierarchical privacy-aware learning framework for real-time quantification of distributed PV active power flexibility

Age‐Aware Energy and Resource Optimization for Federated Air Quality Monitoring With UAVs

Reliable air quality monitoring in Internet of Things (IoT) systems requires a synergy between energy‐efficient data collection, data freshness and scalable learning across geographically distributed sensors. We propose AERO‐Air (Age‐aware Energy and Resource Optimization), a novel hierarchical framework that jointly o...

Differential Privacy for Federated Learning with Adaptive Noise

Federated learning (FL) presents a promising paradigm for training machine learning models on decentralized data sources without directly exchanging the data itself. However, the inherent privacy risks associated with aggregating model updates introduce significant challenges. Traditional differential privacy (DP) tech...

zk-DEAP-artifact-1.0

Secure aggregation requires operating on private inputs while preserving confidentiality, verifying that each input satisfies constraints, and avoiding trusted parties. This scheme appears in federated learning, as well as Internet of Things, operational technology, blockchain, smart-grid, and voting deployments. Exist...

Zero-Knowledge Governance: Cryptographic Compliance Auditing for Federated Edge AI

The rapid expansion of Federated Learning (FL) within Edge AI has enabled decentralized networks to collaboratively train sophisticated models without sharing raw, privacy-sensitive data [8]. However, this strict adherence to data privacy creates a significant "blind spot" for AI governance. Under emerging regulatory f...

A perspective on federated foundation models in biomedical sensing and imaging

In this perspective paper, we introduce multimodal multi-task federated foundation models (M3T FedFMs) as a paradigm for privacy-preserving and distributed learning over biomedical sensing and imaging data. We outline their architecture, applications across medical sectors, key challenges, and future research direction...

Training Without Gathering the Data: A Contemporary Synthesis Review of Federated Learning and Privacy-Preserving AI

This article presents a narrative review of Federated Learning and Privacy-Preserving AI in the context of Artificial Intelligence. The literature on this topic has expanded substantially over recent decades, yet it remains fragmented across subfields, methods, and national research traditions. Drawing on an interpreti...

FedSCORE-PP: A Federated and PrivacyPreserving Machine Learning Framework for Collaborative Supply Chain Risk Prediction Across Organizations

Global supply chains are increasingly exposed to disruptions whose effects propagate across organizational boundaries, yet the data needed to predict such risks is fragmented among firms reluctant to share it for competitive, contractual, and regulatory reasons. Centralized machine learning therefore under-utilizes col...

Transformer-Based Federated Learning for Adaptive Multi-Stage Constant Current Charging (MSCCC) of Li-ion, NiCd, and NiMH Batteries

Reproducibility Package for "Federated Learning-enabled Intrusion Detection System (FL-IDS) with Differential Privacy for Resource-Constrained IoT Networks"

This repository contains the source code, executed notebook, experimental configurations, seed-level results, privacy-accounting outputs, clipping diagnostics, preprocessing metadata and supporting materials for the study “Federated Learning-enabled Intrusion Detection System (FL-IDS) with Differential Privacy for Reso...

A Federated Deep Learning Framework for Privacy-Preserving IoT-Based Healthcare Monitoring Systems

The results show that the suggested federated strategy can reduce per-round communication volume by an order of magnitude, eliminate the need to transmit raw sensor data, and approach centralized-training accuracy within a narrow margin.

Zero-Knowledge Governance: Cryptographic Compliance Auditing for Federated Edge AI

The rapid expansion of Federated Learning (FL) within Edge AI has enabled decentralized networks to collaboratively train sophisticated models without sharing raw, privacy-sensitive data [8]. However, this strict adherence to data privacy creates a significant "blind spot" for AI governance. Under emerging regulatory f...