Tag: non-IID data

A federated Decision Transformer reached 92.5% scheduling accuracy in a simulated industrial environment while reducing parameter transmission by 63% relative ...

A new federated framework called FedAE-KMeans trains autoencoders locally on each device and clusters compressed latent representations on a central ...

A particle swarm optimization strategy that dynamically selects the most informative clients in federated learning matches state-of-the-art methods for medical ...

A systematic review of 128 studies finds that secure federated learning in medical IoT requires layered defenses that balance poisoning ...

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

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

Researchers at Jerash University have developed an energy-aware federated deep learning framework that cuts per-round energy use by about 43 ...

A new federated learning framework called FedGPA balances global generalization and client personalization by aggregating neural network layers at different ...

Researchers at Donghua University have developed RFLDefense, a layer-wise defense framework that suppresses backdoor attacks in federated learning even when ...

A massive 641-run study shows that trust-weighted federated learning can keep a tiny on-device IoT intrusion detector accurate under non-IID ...