federated learning

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...

ARTIFICIAL INTELLIGENCE-BASED ADAPTIVE DATA ARCHITECTURE FOR SCALABLE LOW-CODE INFORMATION SYSTEMS

The rapid development of artificial intelligence (AI), cloud computing, data-intensive applications, and low-code development platforms is changing the architectural requirements of modern information systems. Low-code technologies reduce the amount of manually written software code and enable a wider group of users to...

ARTIFICIAL INTELLIGENCE-BASED ADAPTIVE DATA ARCHITECTURE FOR SCALABLE LOW-CODE INFORMATION SYSTEMS

The rapid development of artificial intelligence (AI), cloud computing, data-intensive applications, and low-code development platforms is changing the architectural requirements of modern information systems. Low-code technologies reduce the amount of manually written software code and enable a wider group of users to...

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...

FEDCODE: A Framework and Experimental Study of Model-Update Compression for Communication-Efficient Federated Learning

Federated learning trains shared models without centralizing local data, but repeated model exchange can become a bottleneck in bandwidth-, energy-, and latency-constrained edge systems. This paper presents FEDCODE, a communication-aware federated learning simulation framework for reproducible evaluation of update repr...

A resource-aware framework for energy and bandwidth efficient IoT intrusion detection using lightweight federated learning over SDN

The rapid growth of Internet of Things (IoT) deployments has widened the network attack surface while increasing the cost of centralised intrusion detection, particularly when raw traffic must be moved to remote servers. We propose HFL-SDN-IDS, a hierarchical, resource-aware framework that combines a lightweight federa...

Integrating Blockchain and AI for Secure and Scalable Financial Transaction Monitoring

The global financial system processes trillions of dollars in transactions daily, creating an urgent need for monitoring systems that are secure, scalable, privacy-preserving, and capable of detecting sophisticated fraud in real time. Despite a decade of development, existing approaches are fundamentally fragmented: ru...