3 accepted papers
Federated learning (FL) allows multiple data-owners to collaboratively train machine learning models by exchanging local gradients, while keeping their private data on-device. To simultaneously enhance privacy and training efficiency, recently parameter-efficient fine-tuning (PEFT) of large-scale pr…
Collaborative machine learning enables privacy-preserving training of machine learning models without collecting sensitive client data. Despite recent breakthroughs, communication bottleneck is still a major challenge against its scalability to larger networks. To address this challenge, we propose…
Federated learning is a distributed framework for training a machine learning model over the data stored by wireless devices. A major challenge in doing so is the communication overhead from the devices to the server. Over-the-air federated learning is a recent framework to address this challenge, w…