federated learning

Source Code for Federated Edge Learning over Optical-UAV Access Links with Deterministic-Payload Compression and Packetized Scheduling

This code-only package accompanies the manuscript “Federated Edge Learning over Optical-UAV Access Links With Deterministic-Payload Compression and Packetized Scheduling.”The software implements static mixed-resolution compression with error feedback (SMR-EF) and PacketFair scheduling for simulated federated learning o...

Resilient Edge-Mesh Federated Learning for LPWAN-Based Urban Flood Monitoring

Federated MapReduce fractional deep learning for scalable and privacy-aware intrusion detection in cloud computing

Conventional intrusion detection systems (IDSs) are not suitable for large-scale, heterogeneous, and privacy-sensitive cloud environments, owing to the rapid adoption of cloud computing and Internet of Things (IoT) technologies, which have resulted in an increasing scale and complexity of cyberattacks. Current machine...

Artificial Intelligence-Driven Design and Optimization of Advanced Drug Delivery Systems: From Formulation Prediction to Clinical Translation: A Comprehensive Review

Advanced drug delivery systems (DDS) — including polymeric and lipid nanoparticles, lipid nanoparticles (LNPs) for nucleic acid therapeutics, solid oral dosage forms, and three-dimensional (3D)-printed and stimuli-responsive platforms — require the simultaneous optimization of dozens of interacting formulation and proc...

Artificial Intelligence-Driven Design and Optimization of Advanced Drug Delivery Systems: From Formulation Prediction to Clinical Translation: A Comprehensive Review

Advanced drug delivery systems (DDS) — including polymeric and lipid nanoparticles, lipid nanoparticles (LNPs) for nucleic acid therapeutics, solid oral dosage forms, and three-dimensional (3D)-printed and stimuli-responsive platforms — require the simultaneous optimization of dozens of interacting formulation and proc...

Training Machine Learning Models at the Edge: A Survey

Edge computing has gained significant traction in recent years, promising enhanced efficiency by integrating artificial intelligence capabilities at the edge. While the focus has primarily been on the deployment and inference of Machine Learning (ML) models at the edge, the training aspect remains less explored. This s...

Privacy-preserving Technologies for Artificial Intelligence

Privacy remains a significant barrier to unlocking the full potential of AI in sensitive domains such as healthcare. In this chapter, we survey methods for preserving privacy while enabling useful data analysis. We review anonymization approaches, including k-anonymity, differential privacy, and federated learning, and...

Deployment-Oriented Deep Learning for Predictive Maintenance of Electric Motor-Driven Rotating Systems: A Review of Robustness, Trustworthiness, and Industrial Readiness

Interoperability and Scalability Challenges

Internet of Things technology, machine learning, and cloud-computing technologies are converging, introducing new opportunities in personalized care, predictive diagnostics, and intelligent decision support in the health sector, which is developing at a very high pace. The only restrictions to its possible application...

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...