Federated learning enables collaborative training of a model while kee** the training data
decentralized and private. However, in IoT systems, inherent heterogeneity in processing …

Pretrained large language models (LLMs) have emerged as a cornerstone in modern
natural language processing, with their utility expanding to various applications and …

The privacy-sensitive nature of decentralized datasets and the robustness of eXtreme
Gradient Boosting (XGBoost) on tabular data raise the needs to train XGBoost in the context …

Federated Learning (FL) is an evolving paradigm that enables multiple parties to
collaboratively train models without sharing raw data. Among its variants, Vertical Federated …

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 …

Vertical Federated Learning (VFL) is a privacy-preserving distributed learning paradigm
where different parties collaboratively learn models using partitioned features of shared …

Vertical Federated Learning (VFL) is a federated learning paradigm where multiple
participants, who share the same set of samples but hold different features, jointly train …

Current state-of-the-art spatial reasoning-enhanced VLMs are trained to excel at spatial
visual question answering (VQA). However, we believe that higher-level 3D-aware tasks …

Federated Learning (FL) is a data-minimization approach enabling collaborative model
training across diverse clients with local data, avoiding direct data exchange. However, state …

Federated learning (FL) is a collaborative machine learning paradigm which ensures data
privacy by training models across distributed datasets without centralizing sensitive …