federated learning

DP-FedDyR: Differential Privacy Federated Learning Considering Privacy and Utility Trade-Offs

Artificial Intelligence Models for Early Disease Detection Using Clinical Data

In a variety of chronic and acute diseases, earlier detection of disease is a major determinant of better patient outcomes, better treatment costs savings and reduced mortality. The current diagnostic process, mostly manual, manual interpretation of clinical data, is limited by the capacity of the human mind, by inter-...

Adaptive TinyML at the Edge: A Multi-Seed Empirical Study of Drift, Robustness, Energy, Federated Learning, and Explainability

Functional MRI Biomarkers of Addiction-Related Neuroplasticity in Neurological Disorders: A Comprehensive Review of Functional Connectivity, Brain Network Reorganization, and Clinical Applications

Recent advances in resting-state functional MRI have enabled non-invasive characterization of large-scale functional networks, providing novel biomarkers for diagnosis, prognosis, and treatment monitoring.Functional magnetic resonance imaging (fMRI)-particularly resting-state functional connectivity (rsFC) and graphthe...

Artificial Intelligence–Driven Intrusion Detection Systems: A Systematic Review of Machine Learning, Deep Learning, and Adaptive Ensemble Approaches

This is a critical systematic review of 40 peer-reviewed articles published between 2021 and 2025 on artificial intelligence-enhanced Intrusion Detection Systems (IDS).To maintain rigour and transparency in the review, the PRISMA 2020 and Kitchenham guidelines are adhered to.The appropriate literature was also searched...

Learning-enabled collaborative intelligence in search and rescue robotics: a survey of paradigms, models, and challenges

Search and Rescue (SAR) robotics increasingly depends on collaborative intelligence to operate effectively in dynamic, uncertain, and time-sensitive environments. Modern SAR missions require coordinated interaction among heterogeneous robotic agents as well as structured collaboration between humans and intelligent sys...

Federated Distillation: A Survey

Federated learning (FL) enables collaborative model training without centralizing clients’ raw data, but parameter exchange can impose substantial communication costs and usually requires compatible model architectures. Federated distillation (FD) instead transfers predictive or intermediate knowledge through knowledge...

Federation Opacity Makes Medical Machine Learning’s Failures Harder to Detect and Correct

Our target article argued that federated learning (FL) generates a distinctive variety of opacity—federation opacity, in which no party can access, analyze, or curate the model’s full training data...

MIRR-FL: code, data and results for 'Federated Blockchain Learning for Automated Medical Incident Root-Cause Analysis'

Code, data and result files behind every number, table and figure of the article Federated Blockchain Learning for Automated Medical Incident Root-Cause Analysis: Solidity smart contracts and tests, the TLA+ model of the incident lifecycle and its TLC results, the synthetic incident generator and dataset, the federated...

An end‑to‑end multi‑source heterogeneous data fusion and spatiotemporal graph convolutional network for enterprise financial crisis early warning

In the early warning of enterprise financial crisis, the difficulty of multi-source heterogeneous data fusion and the complexity of risk transmission effect modeling are the key challenges. To this end, this study proposes an end-to-end deep learning framework named MFGCN to achieve accurate early warning. This paper p...

An Optimization Framework for Digital Advertising Budget Allocation Based on Multi-Objective Distributed Federated Learning and Cloud Computing

The Algorithmic Energy Crisis

The emergence of AI all over the world has significant environmental expenses. The CO2 emission of training big models is the same as five cars operating throughout their lives, and the computational needs are doubling after every 3.4 months. This chapter promotes federated learning (FL), which is capable of reducing d...