Researchers have developed Fed-A²LoRA, a federated fine-tuning method that halves trainable adapter parameters and cuts communication costs by up to...

A new phased client selection method for federated learning improves accuracy, cuts training time and communication costs, and strengthens defenses...

A new reinforcement learning framework called FLASH-DRM selects the most valuable devices for each federated learning round, improving accuracy, latency...

A new federated learning framework lets hospitals train chest X-ray diagnostic AI collaboratively without sharing patient data, and it outperforms...

Researchers have built a federated learning system that combines CKKS homomorphic encryption with blockchain storage, cutting adversarial attack success from...

Researchers have developed a federated learning-based caching scheme that predicts socially popular content and pre-positions it at the network edge...

A new framework combining adaptive fuzzy logic and federated deep reinforcement learning cuts energy consumption in vehicular edge computing by...

Researchers at Vanderbilt University have developed a resilient adaptive aggregation method that enables peer-to-peer machine learning networks to reach consensus...

Researchers have developed GRAB-FL, a graph-aware federated learning framework that uses graph neural networks to assign trust scores to client...

A new study introduces auditable update-utility certificates that verifiably measure the value of each client's contribution in personalized federated learning...