Tag: non-IID data

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 ...

Researchers at Shanghai Polytechnic University have developed CDAFIM, a class-difficulty-aware incentive mechanism that uses Shapley values to fairly reward federated ...

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

Researchers have unveiled a federated learning framework that detects DDoS attacks across IoT networks with up to 96.95 percent accuracy ...