federated learning

25 Years of Cardiac Ion Channel QSAR: From hERG Dominance to Multi-Channel Modeling

Abstract In 2013 the Comprehensive In Vitro Proarrhythmia Assay (CiPA) initiative proposed multi-channel assessment for proarrhythmic risk evaluation, yet ligand-based predictive modeling has remained largely single-channel. To quantify and explain this mismatch, we survey 155 ligand-based modeling studies on cardiac i...

A federated learning and decision transformer-based multi-source data fusion architecture for real-time industrial scheduling dashboards

With the rapid development of industrial IoT and intelligent scheduling systems, real-time fusion of multi-source heterogeneous data and decision optimization have become critical challenges. Traditional centralized data collection methods face privacy leakage and communication bottlenecks, while federated learning (FL...

Asymmetric class-conditional routing in a federated quantum mixture-of-experts improves imbalanced intrusion detection in edge industrial IoT networks

Intrusion detection systems (IDS) deployed in Edge Industrial Internet of Things (Edge-IIoT) environments face three critical and interrelated challenges: (i) extreme class imbalance, where tail-class attack categories may be outnumbered by benign traffic at ratios exceeding 700:1; (ii) competitive expert collapse in g...

Integrating Blockchain and AI to Improve Distributed Storage Systems: Enhancing Cybersecurity and Data Integrity in Cloud Environments

Cloud computing&s;s data explosion demands robust security for distributed storage, where traditional systems falter in transparency and fault tolerance. This paper proposes a blockchain-AI hybrid architecture for resilient, trustless systems. Blockchain enables tamper-proof logs, smart contract access control, and dat...

Simulation of a Fixed-Magnitude Data Poisoning Attack in a Federated Learning Distributed Model

Proactive Smart Policing in the 6G Era

For 6G telecommunications, there needs to be a paradigm shift in public safety. This chapter introduces a methodological approach to proactive, intelligence-based policing, as a strategy that is able to address new threats such as quantum computing and advanced malware that pose problems to traditional reactive policin...

Data Auditing and Quality Assurance in a Federated Learning Consortium; Getting the Best of Both Worlds from Cross-Institutional and In-House Data Quality Inspection

Introduction: Rare and heterogeneous disease research increasingly relies on privacy-enhancing technologies such as federated learning (FL) to enable cross-institutional collaboration across fragmented datasets. However, data quality assurance in FL can be limited, as individual-level data may not be directly accessibl...

IoT-Enabled Threat Detection for Large-Scale Distributed Networks in Smart Policing

The purpose chapter objectives to design a scalable and intelligent threat detection system to smart policing networks that leverages the IoT technology at scale. It will deal with the problems of timely processing of data of various types of devices and security issues related to such devices. It is suggested to use a...

Privacy-preserving and robust mouse dynamics authentication using hybrid transformer-CNN and federated learning

Traditional methods like passwords and PINs are increasingly vulnerable, making continuous authentication essential. Behavioral biometrics such as mouse dynamics provide a non-intrusive way to verify users through their unique interaction patterns. This study proposes a secure and privacy-preserving framework for mouse...

ML for Cybersecurity Predictions in Smart Infrastructure

The proliferation of connected sensors, edge devices, and cloud services has made smart infrastructure cybersecurity threats more prevalent than solutions. In this research, we see how ML models may prevent and detect abnormalities, malware, insider attacks, and distributed denial of service (DDoS). One way to decrease...

Digital Forensics in IoT-Enabled Heterogeneous and Intelligent Network Environments

The excessive proliferation of Internet of Things (IoT) ecosystems, which are characterized by the high number of devices, transient data generation, and constantly changing cyber threats have presented serious challenges to digital forensic investigations. To solve these problems, Adaptive Forensic Intelligence Model...

BAsyncFed-IDS: A buffered asynchronous federated learning-based intrusion detection system for in-vehicle networks

The Controller Area Network (CAN) is highly susceptible to malicious attacks due to its lack of encryption and authentication mechanisms, leading to the necessity of intrusion detection systems (IDS). However, centralized data processing not only incurs high storage costs and bandwidth overhead but also aggravates priv...