federated learning

Blockchain enabled federated learning for privacy preserving medical data processing in healthcare as a systematic literature review

A PRISMA-based Systematic Literature Review was conducted to synthesize evidence from 37 peer-reviewed studies focusing on blockchain-enabled federated learning for healthcare data privacy, security, and preservation, indicating that the integration of blockchain and federated learning offers significant potential for...

The complexities and guises of anonymity: privacy-enhancing technologies and medical AI

As investment in artificial intelligence (AI) grows, so too do debates about how to enable the secondary use of patient data to train AI models. Privacy-enhancing technologies (PETs) such as synthetic data, federated learning, and Secure Data Environments (SDEs) have been put forward as technical solutions to facilitat...

Blockchain-Anchored Reputation for Robust Aggregation in Homomorphically Encrypted Federated Learning — Code and Data

Blockchain-Anchored Reputation for Robust Aggregation in Homomorphically Encrypted Federated LearningCode, data splits, results and figures. Changes in version 1.1.0 Added code/tcga/, the preprocessing, audit, baseline and robustness scripts for the second corpus, and nibabel to requirements.txt. Added the second-corpu...

AFHAL: Adaptive Federated Hybrid-Attention Learning for privacy-aware medical image segmentation

CESFL-ECG: communication-efficient split federated learning for patient-independent ECG classification

Code, per-run results, tables and figures for the manuscript "Communication-efficient split federated learning for patient-independent ECG classification". CESFL-ECG quantizes the smashed data and cut-layer gradients of split federated learning to 4 bits with stochastic rounding and is evaluated on MIT-BIH (DS1/DS2, fi...

DeadOrSlow: silence-aware client failure detection for semi-synchronous federated learning (code, raw logs and design log)

Reproducibility artifact for the manuscript "Dead, Hung, or Just Unreachable? Silence-Aware Client Failure Detection for Semi-Synchronous Federated Learning" (Z. Kutlu). Contents: the detector code shared by the online FL server and the offline trace replay; the FL server and Raspberry Pi client; the fault and network-...

DDoS attacks detection for NoN-IID data using sequential federated transfer learning

Problem-to-Outcome Architecture: A Federated Methodology for Converting Operational Problems into Measurable Outcomes

Problem-to-Outcome Architecture is a federated methodology for preserving continuity from an operational problem signal to measurable deployment outcomes and reusable knowledge. It separates problem discovery from technology-task formulation; links persistent problems to continuous technology intelligence; supports bid...

AI- Driven Adaptive Resource Allocation For Next- Generation IoT Communication Networks

The emergence of Internet of Things (IoT) devices in next-generation communication networks has brought about new and complex challenges related to resource management, which include massive connectivity, heterogeneous traffic loads, and strict energy considerations. Static and heuristic resource allocation algorithms...

DeadOrSlow: silence-aware client failure detection for semi-synchronous federated learning (code, raw logs and design log)

Reproducibility artifact for the manuscript "Dead, Hung, or Just Unreachable? Silence-Aware Client Failure Detection for Semi-Synchronous Federated Learning" (Z. Kutlu and R. K. Ağca). Contents: the detector code shared by the online FL server and the offline trace replay; the FL server and Raspberry Pi client; the fau...

DeadOrSlow: silence-aware client failure detection for semi-synchronous federated learning (code, raw logs and design log)

Reproducibility artifact for the manuscript "Dead, Hung, or Just Unreachable? Silence-Aware Client Failure Detection for Semi-Synchronous Federated Learning" (Z. Kutlu and R. K. Ağca). Contents: the detector code shared by the online FL server and the offline trace replay; the FL server and Raspberry Pi client; the fau...

Foundations of Human-Centric AI and Machine Learning for Construction 6.0

The foundations of Human-Centric Artificial Intelligence and Machine Learning as enablers of Construction 6.0, emphasizing the integration of automation, cognitive systems, and human expertise are examined. The chapter outlines how Artificial Intelligence is transforming architecture, engineering, and construction thro...