federated learning

Evolving federated density peak clustering with shuffler-based anonymization

Abstract In this paper, we present a novel one-shot federated clustering approach based on evolving fuzzy clustering and shuffler-based anonymization. In federated clustering, each round reveals additional model information to the server and can increase the opportunity to infer properties of local datasets, even when...

A Privacy-Preserving Decentralized Partition-Autonomous Planning Method for Interconnected Multi-Microgrid Distribution Systems Driven by Federated Proximal Policy Optimization

INTRODUCTION: The rapid integration of distributed renewable energy and ICT infrastructure has made interconnected multi-microgrid distribution systems essential for low-carbon power grids, yet centralized coordination exposes sensitive operational data to privacy risks, including eavesdropping and membership inference...

Comparison and Contrast of two Machine Learning Assisted In-Device Scheduling Applications

Managing offloaded tasks efficiently is vital for keeping devices fast and responsive. The main hurdle is finding a way to schedule and run these incoming workloads without starving local applications of critical CPU, RAM, and energy. This study evaluates how Federated Learning (FL) and Reinforcement Learning (RL) can...

Methodology Review: Intrusion Detection in Cybersecurity: A Study on Explainable Graphical Reinforcement Learning

This paper provides a structured methodological review of intrusion detection systems (IDS) in the field of cybersecurity, with particular attention to Explainable Graph-Structured Reinforcement Learning (XGRL). Instead of presenting experimental evaluations, the study concentrates on analyzing design principles and me...

AI for Solar Energy Systems

The rapid expansion of solar energy has accelerated the adoption of Artificial Intelligence (AI) to improve forecasting accuracy, photovoltaic performance, intelligent control, fault diagnosis, energy storage management, and smart grid operation. Recent advances in machine learning, deep learning, Digital Twins, Physic...

Role of Artificial Intelligence, Machine Learning, and Deep Learning in Transforming Public Health Services: Opportunities, Challenges, and Future Directions

Advanced public health services are leveraging Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) to revolutionize their functions by harnessing complex data for analysis, forecasting of disease outbreaks, and making intelligent decisions. They are covered in this review and used in epidemiolog...

Is Non-IID an Artifact of Training from Scratch? A Critical Review of Data Heterogeneity in Federated Learning in the Pretraining Era

THE EL-RAKHAWI COMPREHENSIVE FRAMEWORK FOR PRECISION DETECTION AND TARGETED NANOTHERAPY OF LARYNGEAL CANCER WITH VOCAL FUNCTION PRESERVATION A Multidisciplinary Theoretical Framework Unifying Artificial Intelligence-Driven Acoustic Biomarkers, Liquid Biopsy, Nanoparticle-Targeted Therapy, and Regenerative Medicine for Laryngeal Squamous Cell Carcinoma

The El-Rakhawi Comprehensive Framework for Precision Detection and Targeted Nanotherapy of Laryngeal Cancer (DOI: 10.5281/zenodo.23297264) presents a groundbreaking multidisciplinary framework across six volumes: (1) Precision Detection via AI acoustic biomarkers and salivary liquid biopsy; (2) Targeted Nanotherapy usi...

BOF: Trustworthy Privacy-Preserved Federated Learning for Science

The session Federated learning offers a promising approach for enabling collaborative scientific discovery while preserving the privacy of sensitive data across institutions. This BOF brought together researchers and practitioners to discuss trustworthy, privacy-preserving federated learning frameworks tailored for sci...

Enhancing network science with large language models and federated artificial intelligence

Abstract Sixth generation (6G) networks require intelligent and privacy-preserving solutions to handle traffic complexity, resource allocation, and security challenges. While Large Language Models (LLMs) offer advanced reasoning and Federated Artificial Intelligence (FAI) enables distributed learning without directly s...

Deep Learning-Based Liveness Detection in Multimodal Biometric Systems: A Systematic Review of Presentation and Indirect Attacks

An Efficient and Privacy-Preserving Framework for Edge AI Using Federated Learning and Homomorphic Encryption

Edge Artificial Intelligence (Edge AI) supports intelligent processing close to where data is generated. However, training machine-learning models across edge devices is difficult when devices have limited memory, processing power, battery capacity, and network bandwidth. Federated learning (FL) addresses one important...