federated learning

Decentralized Federated Learning with Differential Privacy for Edge Computing

Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging data. However, traditional FL methods often rely on a central server, raising significant privacy concerns, especially when dealing with sensitive data residing on edge devic...

Decentralized Federated Learning with Secure Aggregation and Differential Privacy

Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without direct data sharing. However, traditional FL schemes are vulnerable to privacy attacks, particularly model inversion attacks, which can reveal sensitive information about the underlying data. Th...

Client clustering versus personalization in federated residential load forecasting: A personalization-fair benchmark of accuracy and communication overhead

Residential load forecasting underpins demand response and local energy trading, yet privacy regulations such as the EU GDPR prevent service providers from collecting fine-grained household consumption data. Federated learning (FL) enables collaborative training without raw data exchange, and client clustering widely m...

Decentralized Federated Learning with Secure Aggregation and Differential Privacy

Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without direct data sharing. However, traditional FL schemes are vulnerable to privacy attacks, particularly model inversion attacks, which can reveal sensitive information about the underlying data. Th...

APPLICATION OF ARTIFICIAL INTELLIGENCE IN REGULATORY COMPLIANCE FOR CROSS-BORDER DIGITAL TRANSACTIONS

This study examines the theoretical, structural, and empirical applications of Artificial Intelligence (AI) and Machine Learning (ML) architectures within the domain of regulatory compliance (RegTech) and supervisory technology (SupTech) for cross-border digital transactions. The exponential expansion of cross-border f...

Distributed Differential Privacy with Federated Learning via Lagrangian Relaxation

Achieving strong differential privacy guarantees within the constraints of federated learning, especially when utilizing complex models, remains a significant challenge. This work proposes a novel framework leveraging Lagrangian relaxation to address this issue. The core idea involves incorporating a Lagrangian term di...

Scalable Microservices and Data Integration for Modern eCommerce and Enterprise Systems

Modern eCommerce and enterprise systems have become more complex, which has prompted the use of the microservice architecture and sophisticated data integration strategies to provide scalability and resilience, as well as speed in the delivery of features. This chapter consolidates important architectural patterns, int...

Human centered artificial intelligence for smart city traffic coordination

A human-centric edge intelligence framework that integrates federated reinforcement learning (FRL) with interpretable human–machine interfaces for urban traffic coordination and provides a practical pathway for evaluating intelligent mobility systems that balance decentralized optimization, privacy preservation, interp...

Decentralized Federated Learning with Byzantine Fault Tolerance

Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly sharing the data itself. However, this decentralized nature introduces significant vulnerabilities. Malicious participants, known as Byzantine nodes, can inject biased or corrupted mode...

A scalable and privacy-aware framework for connected vehicle systems using smartphone-based multimodal sensing

Connected Vehicle (CV) networks have emerged as key enablers of next-generation intelligent transportation systems, improving road safety and operational efficiency through real-time data exchange. However, large-scale adoption remains limited due to two fundamental challenges: 1) an unclear stakeholder value propositi...

Energy-efficient federated deep learning for edge-based image recognition in resource-constrained IoT environments

Experimental evaluation on MNIST, CIFAR-10, and Tiny ImageNet under a controlled federated simulation indicates that the proposed Federated CNN attains classification accuracy within 2.1 percentage points of a centralized baseline and reduces average per-round energy by approximately 43% relative to centralized trainin...

Bridging Research and Practice in Federated Learning: A Multivocal Review of System-Level Architectures and Processes