A new study shows federated learning's average accuracy can mask severe failures for individual clients, and that server-side re-weighting destabilizes...

A new decentralized federated learning framework uses particle swarm optimization to dynamically select clients, reporting 76.2% accuracy on CIFAR-10 under...

FedSPA combines Shapley-value analysis and model parameter similarity to identify malicious clients in federated learning under data poisoning attacks.

A new framework for privacy-preserving healthcare analytics, published in Discover Artificial Intelligence, reports measurable gains in accuracy, latency, and communication...

A new open-access framework combines graph neural networks, LSTM models, large language models and federated learning to detect money laundering...

A new framework called OceanWeave combines Ocean Protocol data markets, sharded compute-to-data consensus, and encrypted federated learning, reporting 3,200 TPS...

Researchers propose CROG and CLIG, two aggregation algorithms that weight client updates by Euclidean gradient distances to stabilize federated learning...

A new EU-focused analysis argues that federated learning reduces some data exposure risks but cannot by itself guarantee privacy or...

A new personalized federated learning framework uses Transformer-based dynamic aggregation and adaptive regularization to improve colorectal cancer CT image classification...

A simulated federated learning framework combines carbon-budgeted client selection, blockchain auditing, and gradient-boosting ensembles for cardiovascular prediction.