federated learning

VertiMosaic

A reproducible research framework for vertical federated learning on heterogeneous tabular data with explicit privacy boundaries, provenance and communication auditing.

FedPath-MTL: A Federated Multi-Task Framework for Personalized Learning Pathways with Real-Record Predictive Evaluation

Personalized learning pathways are difficult to support in distributed educational systems because learner records cannot always be centralized and one shared federated predictor may not represent heterogeneity across institutions, learning tasks, and individual learners. We propose FedPath-MTL, a federated multi-task...

Decentralized Gradient Verification in Federated Learning Via Recursive zk-SNARKs

A Novel Hybrid Deep Learning Method for Early Detection of Lung Cancer Using Optimized Deep Neural System

Lung cancer remains one of the deadliest cancers in worldwide, largely because it is often diagnosed late, when treatment options are limited. Computed Tomography (CT) imaging supports early screening, but manual interpreting these scans manually is slow and produces inconsistent results across different radiologists....

FedIoMT-RPM v3.0: Lightweight adaptive split federated learning for patient-independent ECG arrhythmia classification

extra_baseline_models\ README.txt (updated) requirements.txt common_fl.py common_eval.py common_dataset.py make_ds1ds2_data.py FedIoMT_RPM_RecordIndependent.py Centralized_CNN_ / FedAvg_ / FedProx_ / SCAFFOLD_ / CSFL_ / SplitFed_RecordIndependent.py run_all_seeds.py run_improvement.py summarize_results.py run_noise_def...

Marchés cibles 2024–2030 pour produits IA

Abstract ENThis document, produced with the assistance of ChatGPT o3 and ChatGPT 5 Thinking, is released under the Apache 2.0 licence. It is a voluntary defensive publication (prior art) and therefore enters the prior art upon release under the applicable patent statutes: (art. L 611-11 CPI / art. 54(2) CBE), 35 U.S.C....

PLANT FRUIT DISEASE SEVERITY ALERT NETWORK USING HYBRID DEEP LEARNING AND A RETRIEVAL-AUGMENTED GENERATION MODEL

Agricultural fruit production is significantly affected by plant diseases that reduce crop yield, degrade fruit quality, increase pesticide dependency, and threaten global food security. Disease symptoms vary considerably in lesion texture, shape, color, infection spread, and environmental background, making accurate d...

FedStaP: code, trained models and results for non-IID federated intrusion detection (CICIoT2023, Edge-IIoTset)

Research artefact for the study FedStaP: Shared Feature Statistics and Prior-Calibrated Training for Non-IID Federated Intrusion Detection in IoT Networks. Contents. The full implementation (Hydra configurations, federated simulator, the proposed FedStaP method, ten global-model baselines, a personalised reference and...

A hybrid deep learning framework for privacy-preserving sepsis prediction in distributed ICU environments using federated learning simulation

Sepsis remains one of the leading causes of intensive care unit (ICU) mortality in the United States, and timely clinical decision support is essential for improving patient outcomes. Although Electronic Health Record (EHR) systems provide access to large volumes of clinical data, strict patient privacy regulations and...

FedCareX: Trust-Aware Federated Transformer for Wearable IoT Seizure Prediction

Behind-the-ear electroencephalography (EEG) can now be recorded continuously outside hospital, but forecasting the preictal state is hard in a distributed deployment: raw traces cannot leave the clinical site, the wearable montage is chosen per patient, and part of the labelling comes from automated annotators rather t...

Federated AI, Health Data Interoperability, and Digital Twins in Africa: A Framework for Privacy-Preserving Precision Healthcare in Resource-Limited Settings

While Africa has a disproportionately high share of the global disease burden, it is also plagued by poor connectivity, a lack of genomic reference data, and diverse digital and network infrastructure that impede the continents shift toward precision healthcare. To work around these constraints, three converging techno...

appunuarni1975-spec/FedIoMT-RPM: FedIoMT-RPM v3.0: Lightweight adaptive split federated learning for patient-independent ECG arrhythmia classification

FedIoMT-RPM v3.0: Lightweight adaptive split federated learning for patient-independent ECG arrhythmia classification This release contains the complete code and final results for the revised manuscript submitted to Scientific Reports. It replaces v2.0. Main changes from v2.0 Patient-independent evaluation. All methods...