federated learning

Review of: "Advancing Personal Health Data Spaces: Governance, Interoperability, and Real-World Evidence for Learning Healthcare Systems"

This manuscript presents a timely, ambitious, and highly relevant commentary on the role of Personal Health Data Spaces in the future development of healthcare.I appreciate its patient-centred perspective and its recognition that personalised medicine cannot be achieved through isolated technological solutions, fragmen...

Decentralized Verification of Federated Learning Models

Federated Learning (FL) has emerged as a promising paradigm for training machine learning models on decentralized data sources without direct data sharing. However, ensuring the integrity and accuracy of these models remains a significant challenge. Traditional verification methods often rely on centralized aggregation...

Replication package for Flows and Rounds: benchmark and retrieval scripts for distributed neurodynamic optimization and federated learning

This package contains the code and generated data behind the quantitative results of the survey "Flows and Rounds: A Survey of Distributed Neurodynamic Optimization and Federated Learning", submitted to Neurocomputing. It reproduces every number, table, and figure in Sections 2.3 and 8 of the article. No third-party, p...

A proactive defense framework for federated learning: A framework integrating verified piecewise perturbation and quantum-enhanced encryption

Land subsidence hazard mapping using a federated learning architecture with satellite and local environmental data

Land subsidence has become one of the most critical environmental and geomorphological hazards in recent years, threatening groundwater resources, urban infrastructure, and ecosystem stability. This study presents a novel Federated Learning (FL)-based framework for land subsidence hazard mapping in Poldokhtar County, I...

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...