federated learning

Deep Learning-Based Assisted Diagnosis of Alzheimer's Disease: From Monomodal Imaging to Multimodal Fusion

In the field of auxiliary diagnosis of Alzheimer's disease (AD), deep learning is developing along two paths: single-modal imaging and multi-modal fusion. The single-modal approach utilizes lightweight networks (with efficient receptive fields and multi-axis attention mechanisms) to optimize preprocessing and improve t...

Toward Trustworthy AI for Financial Fraud Detection: A Structured Review of Adversarial Robustness, Privacy Preservation, and Deployment

A federated graph learning method based on personalized prototype aggregation and structure-aware augmentation

Federated Graph Learning (FGL) enables privacy-preserving collaborative training of Graph Neural Networks (GNNs) across distributed clients. In practice, discrepancies in local class distributions and graph structures result in non-independent and identically distributed (non-IID) data across clients, while heterogeneo...

Fine-tuning large language models in resource-constrained environments: Methods and trade-offs

The rapid scaling of large language models has created significant barriers to fine-tuning, with standard approaches requiring computational resources far exceeding typical hardware constraints. A 7-billion parameter model demands roughly 100 to 120GB of memory for full fine-tuning under mixed-precision training, effec...

Beyond accuracy in crop classification: calibration, selective prediction, and robustness

Introduction: Near-perfect crop-label accuracy can conceal uncertainty, perturbation sensitivity, and weak explanations. This study evaluates these reliability dimensions without treating benchmark classification as an agronomic recommendation. Materials and methods: Ten classifiers were evaluated using five matched st...

Data Quality, Feature Engineering, and Model Reliability in Large-Scale AI Multi-Agentic Systems

Large-scale artificial intelligence (AI) systems increasingly operate not as a single monolithic model but as a population of interacting, specialized agents separate models or decision-making components responsible for functions such as pricing, fraud detection, ranking, routing, and customer support that share underl...

Scalable Real-Time Data Pipelines for AI and Machine Learning–Driven Enterprise Systems

The growth of enterprise data volumes across the 2000s and 2010s pushed traditional batch-oriented data processing infrastructures past their practical limits, motivating a sustained shift toward distributed, stream-based architectures capable of supporting real-time analytics and machine learning (ML). This article sy...

SAVANT FRAMEWORK: Defensive Publication v5.0 — Comprehensive Prior Art Establishment for 14 Patent Families

This defensive publication establishes timestamped, citable prior art for fourteen patent families of the SAVANT FRAMEWORK ecosystem: AEGIS-P1–P8 (sovereign LoRa mesh architecture, passive child-safety beacon, vehicle-mounted hostile detection, multi-sensor threat fusion, adaptive anti-jam frequency hopping, sentinel m...

Security and Privacy for Connected Systems: A Layered Review of Cryptography, Intrusion Detection and Privacy-Preserving Learning in IoT, Cloud and Mobile Networks

Connected systems now carry medical readings, financial records and personal data across sensors, mobile networks and cloud servers. Each layer of such a system faces different threats. Small IoT devices cannot run heavy encryption, ad hoc networks change their topology all the time, cloud stores hold large volumes of...

FED-XID: a novel federated learning based architecture for distributed anomaly detection in smart grids

Federated Deep Learning for Privacy-Preserving Data Fusion in Sustainable AI-Driven Healthcare Systems

Integrating Artificial Intelligence and Multicenter Real-World Evidence to Advance Precision Dosing: A Critical Perspective Review.

BACKGROUND Precision dosing integrates patient-specific factors to optimize pharmacotherapy. However, current evidence often relies on limited single-center studies, restricting generalizability. METHODS A narrative review of literature on real-world evidence, pharmacogenetics, biomarkers, artificial intelligence (AI...