federated learning

Privacy-preserving blockchain systems based on homomorphic encryption: design and challenges

This paper investigates recent advances in the integration of HE with blockchain privacy protection, with particular emphasis on representative schemes employing fully homomorphic encryption (FHE) and partially homomorphic encryption (PHE) in scenarios including privacy-preserving smart contracts, federated learning, a...

Design of an English cross-scene model for enhanced mobile multimedia based on federated learning

As mobile multimedia technology in education develops quickly, cross-scenario English teaching has created new challenges for efficient and accurate speech translation and recognition systems. Traditional machine learning methods have obvious deficiencies in privacy protection, data storage, and cross-scenario data uti...

A Noise-Aware Federated Learning Framework for Fish Spoilage Prediction in Small-Scale Fisheries: An Architecture-Controlled Performance Evaluation

Post-harvest fish spoilage remains a major challenge for small-scale fisheries in developing economies, with losses estimated at 25-30% attributed largely to inadequate cold chain monitoring at informal landing sites and markets. This paper presents a noise-aware Federated Learning (FL) framework for predicting fish sp...

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...