12 citations · 44 across the 15 of their papers we have counts for
10 papers · 1 filter
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,…
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…
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…
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…