12 citations · 44 across the 15 of their papers we have counts for
15 papers
Provably Robust Adaptation for Language-Empowered Foundation Models
Yuni Lai, Xiaoyu Xue, Linghui Shen +5
Language-empowered foundation models (LeFMs), such as CLIP and GraphCLIP, have transformed multimodal learning by aligning visual (or graph) features with textual representations,…
ARES: Multimodal Adaptive Reasoning via Difficulty-Aware Token-Level Entropy Shaping
Shuang Chen, Yue Guo, Yimeng Ye +7
Recent advances in multimodal large reasoning models (MLRMs) have substantially improved their ability to solve complex textual and visual tasks. However, these models tend to over…
EmInspector: Combating Backdoor Attacks in Federated Self-Supervised Learning Through Embedding Inspection
Yuwen Qian, Shuchi Wu, Kang Wei +5
Federated self-supervised learning (FSSL) has recently emerged as a promising paradigm that enables the exploitation of clients' vast amounts of unlabeled data while preserving dat…
QuickDrop: Efficient Federated Unlearning by Integrated Dataset Distillation
Akash Dhasade, Yaohong Ding, Song Guo +3
Federated Unlearning (FU) aims to delete specific training data from an ML model trained using Federated Learning (FL). We introduce QuickDrop, an efficient and original FU method…
Compresso: Structured Pruning with Collaborative Prompting Learns Compact Large Language Models
Song Guo, Jiahang Xu, Li Lyna Zhang +1
Despite the remarkable success of Large Language Models (LLMs), the massive size poses significant deployment challenges, particularly on resource-constrained hardware. While exist…
Optimization of Federated Learning's Client Selection for Non-IID Data Based on Grey Relational Analysis
Shuaijun Chen, Omid Tavallaie, Michael Henri Hambali +5
Federated learning (FL) is a novel distributed learning framework designed for applications with privacy-sensitive data. Without sharing data, FL trains local models on individual…
On Knowledge Editing in Federated Learning: Perspectives, Challenges, and Future Directions
Leijie Wu, Song Guo, Junxiao Wang +3
As Federated Learning (FL) has gained increasing attention, it has become widely acknowledged that straightforwardly applying stochastic gradient descent (SGD) on the overall frame…
Towards Unbiased Training in Federated Open-world Semi-supervised Learning
Jie Zhang, Xiaosong Ma, Song Guo +1
Federated Semi-supervised Learning (FedSSL) has emerged as a new paradigm for allowing distributed clients to collaboratively train a machine learning model over scarce labeled dat…
DualMix: Unleashing the Potential of Data Augmentation for Online Class-Incremental Learning
Yunfeng Fan, Wenchao Xu, Haozhao Wang +3
Online Class-Incremental (OCI) learning has sparked new approaches to expand the previously trained model knowledge from sequentially arriving data streams with new classes. Unfort…
FedTune: A Deep Dive into Efficient Federated Fine-Tuning with Pre-trained Transformers
Jinyu Chen, Wenchao Xu, Song Guo +3
Federated Learning (FL) is an emerging paradigm that enables distributed users to collaboratively and iteratively train machine learning models without sharing their private data.…
PMR: Prototypical Modal Rebalance for Multimodal Learning
Yunfeng Fan, Wenchao Xu, Haozhao Wang +2
Multimodal learning (MML) aims to jointly exploit the common priors of different modalities to compensate for their inherent limitations. However, existing MML methods often optimi…
Demystify Self-Attention in Vision Transformers from a Semantic Perspective: Analysis and Application
Leijie Wu, Song Guo, Yaohong Ding +4
Self-attention mechanisms, especially multi-head self-attention (MSA), have achieved great success in many fields such as computer vision and natural language processing. However,…
PromptFL: Let Federated Participants Cooperatively Learn Prompts Instead of Models -- Federated Learning in Age of Foundation Model
Tao Guo, Song Guo, Junxiao Wang +1
Quick global aggregation of effective distributed parameters is crucial to federated learning (FL), which requires adequate bandwidth for parameters communication and sufficient us…
A Survey on Gradient Inversion: Attacks, Defenses and Future Directions
Rui Zhang, Song Guo, Junxiao Wang +2
Recent studies have shown that the training samples can be recovered from gradients, which are called Gradient Inversion (GradInv) attacks. However, there remains a lack of extensi…
Efficient Attribute Unlearning: Towards Selective Removal of Input Attributes from Feature Representations
Tao Guo, Song Guo, Jiewei Zhang +2
Recently, the enactment of privacy regulations has promoted the rise of the machine unlearning paradigm. Existing studies of machine unlearning mainly focus on sample-wise unlearni…