Ons Aouedi

Federated learning can now predict QoS failures in wireless networks, achieving near-centralized performance while preserving user data privacy.

QEF-GT-AdamW achieves superior robustness and convergence in decentralized learning over unreliable wireless networks, setting a new standard for communication efficiency.

Gradient misalignment across devices in parallel split learning can be tamed with a novel gradient alignment strategy, leading to faster convergence and higher accuracy in heterogeneous federated learning.