The reliance of language model training on massive amounts of computation and vast
datasets scraped from potentially low-quality, copyrighted, or sensitive data has come into …

Recently, machine unlearning, which seeks to erase specific data stored in the pre-trained
or fine-tuned models, has emerged as a crucial protective measure for LLMs. However …

Graph hypernetworks (GHNs), constructed by combining graph neural networks (GNNs) with
hypernetworks (HNs), leverage relational data across various domains such as neural …

Personalized subgraph Federated Learning (FL) is a task that customizes Graph Neural
Networks (GNNs) to individual client needs, accommodating diverse data distributions …

Collaborative self-supervised learning has recently become feasible in highly distributed
environments by dividing the network layers between client devices and a central server …

In the field of heterogeneous federated learning (FL), the key challenge is to efficiently and
collaboratively train models across multiple clients with different data distributions, model …

Federated Learning (FL) enables collaborative training of machine learning models on
decentralized data while preserving data privacy. However, data across clients often differs …