arXiv:2610.28545v1 Announce Type: new
Abstract: Decentralised federated learning (DFL) trains personalised models on devices that average with their neighbours rather than with a central server. Real...
By Qiyuan Wang, Ke Xiao, Christos Anagnostopoulos
Decentralised federated learning (DFL) trains personalised models on devices that average with their neighbours rather than with a central server. Real devices, however, are often offline for hours or...
arXiv:2608. 06946v1 Announce Type: cross Abstract: In gossip learning, a network of nodes trains a shared model collaboratively, without a central coordinator, by repeatedly exchanging parts of their local models.
By Fabien Mathieu (NPA), Alexandre Pham (NPA), Maria Gradinariu Potop-Butucaru (NPA), S{\'e}bastien Tixeuil (IUF, NPA)
arXiv:2608. 15215v1 Announce Type: cross Abstract: Token-level knowledge distillation (KD) matches two conditional distributions per position, yet the standard objectives compare them pointwise: a Kullback-Leibler gradient is blind to which wrong token receives probability mass.
By Gordei Verbii, Juho Lee
arXiv:2608.29472v1 Announce Type: cross
Abstract: Knowledge distillation trains a small student model to reproduce the outputs of a large teacher model, and its progress is typically monitored throug...
By Kazuyuki Hara, Hideitsu Hino
arXiv:2610.07655v1 Announce Type: cross
Abstract: Discrete diffusion models have emerged as a practically successful framework for generative modeling on discrete product spaces, yet their statistica...
By Dongsun Yoon, Saptarshi Chakraborty