Tag: privacy-preserving AI

A five-seed simulation study found high detection and mitigation rates for slice-aware DDoS defense, while synthetic 6G features and limited ...

A simulated five-client study found that a compressed federated student-behavior detector retained 95.64% mAP@0.5 and ran at 15.43 FPS on ...

A transformer-autoencoder method that groups federated clients without labels improved sentiment-classification accuracy across controlled label-skew conditions on Sentiment140 and IMDB.

A Paillier-CKKS framework encrypted federated-learning updates and reached 94.67% average accuracy on Medical-MNIST during simulated reverse-engineering attacks.

A divergence-aware federated framework achieved 95.75% accuracy on non-IID email-malware benchmarks while generating visual explanations for its predictions.

Researchers have unveiled FALCON-6G, a framework combining large language models with federated artificial intelligence to deliver faster, more accurate, and ...

A new federated framework fine-tunes the Segment Anything Model for multi-organ segmentation by training only tiny low-rank adapters on quantized ...

Researchers have developed pFedDS, a personalized federated learning approach that measures each model parameter's sensitivity to non-uniform data and adapts ...

Researchers have developed FedHybrid-ViT, a privacy-preserving framework that fuses convolutional and transformer models within federated learning to detect deepfakes across ...

Researchers in Morocco have integrated federated learning with the OneM2M IoT service layer, showing faster, privacy-preserving machine learning at the ...