Physics-informed neural networks (PINNs) are infamous for being hard to train.

Their winter survival trick is a months-long power-save mode--and scientists think it could help humans, too. This bear woke up like this. Breakthroughs, discoveries, and DIY tips sent six days a week. For many animals that live in cold climates, winter means low-power mode. But no creature is more tied to the image of a long, cozy winter than hibernating bears all snuggled up in their dens.

Personalized Federated Graph Learning (pFGL) facilitates the decentralized training of Graph Neural Networks (GNNs) without compromising privacy while accommodating personalized requirements for non-IID participants. In cross-domain scenarios, structural heterogeneity poses significant challenges for pFGL. Nevertheless, previous pFGL methods incorrectly share non-generic knowledge globally and fail to tailor personalized solutions locally under domain structural shift. We innovatively reveal that the spectral nature of graphs can well reflect inherent domain structural shifts. Correspondingly, our method overcomes it by sharing generic spectral knowledge. Moreover, we indicate the biased message-passing schemes for graph structures and propose the personalized preference module.

Neural Information Processing Systems http://nips.cc/