federated learning

Training Without Gathering the Data: A Historical Development Review of Federated Learning and Privacy-Preserving AI

This article presents a narrative review of Federated Learning and Privacy-Preserving AI in the context of Artificial Intelligence. The literature on this topic has expanded substantially over recent decades, yet it remains fragmented across subfields, methods, and national research traditions. Drawing on an interpreti...

A Privacy-Preserving Distributed Machine Learning Aggregation Scheme

A multi-server secure aggregation scheme based on additive secret sharing that achieves information-theoretic privacy protection, effectively breaking the trilemma among privacy, accuracy, and efficiency.

Decentralized Learning with Federated Graph Neural Networks

This paper proposes a novel approach to decentralized learning utilizing Federated Graph Neural Networks (FedGNNs). The core idea is to facilitate collaborative knowledge discovery across a network of devices by employing locally maintained graph representations and periodically synchronized Graph Neural Networks (GNNs...

FedHiPL: Federated Hierarchical Prototype Learning for Heterogeneous Non-IID Data

Federated learning (FL) collaboratively trains models across networked industrial Internet of Things (IIoT) terminals. However, statistical heterogeneity in IIoT data often hinders the performance of global models. Current FL methods typically focus on single-level representation alignment and fail to exploit gradient-...

Federated learning explainable deep learning framework for enhanced internet of medical things security

A novel federated learning (FL) explainable deep recurrent neural network (DRNN)-augmented long short-term memory (LSTM) framework for anomaly intrusion in IoMT systems, named federated recurrent short-term memory.

Engineering a Governance-Aware AI Sandbox: Design, Implementation, and Lessons Learned

This work designs and operationalizes a governance-aware, multi-tenant AI sandbox that supports structured experimentation and produces reusable evaluation evidence across stakeholders and yields lessons learned and practical considerations that inform deployment and future evolution of governance-aware sandbox platfor...

H-FedSN: Personalized Sparse Networks for Efficient and Accurate Hierarchical Federated Learning for IoT Applications

Experiments show that H-FedSN reduces communication costs by up to 477 times compared to baseline methods while maintaining high accuracy, making it well-suited for hierarchical FL in IoT deployments.

SCALABLE SMART CITY PLATFORM USING IOT AND CLOUD COMPUTING

Rapid urbanization has significantly increased pressure on transportation systems, healthcare, energy distribution, environmental monitoring, waste management, public safety, and other municipal services. Traditional city management approaches are increasingly unable to process the enormous volume of heterogeneous data...

SCALABLE SMART CITY PLATFORM USING IOT AND CLOUD COMPUTING

Rapid urbanization has significantly increased pressure on transportation systems, healthcare, energy distribution, environmental monitoring, waste management, public safety, and other municipal services. Traditional city management approaches are increasingly unable to process the enormous volume of heterogeneous data...

Learning from agile software development

Self-organized Learning in Software Factory: Experiences and Lessons Learned

A set of themes that can potentially explain self-organization from the learning viewpoint are identified, which include self-decided learning goals and personalized learning outcomes, peer teaching through active collaboration, diversity is the key and the personal attitude towards the learning matters.

What Can Software Startuppers Learn from the Artistic Design Flow? Experiences, Reflections and Future Avenues

This paper aims contributing to this gap by studying the artistic design flow and the tools utilized by architects, industrial designers and artists, and proposes concrete ways to improve the current state-of-practice.