20 citations · 84 across the 25 of their papers we have counts for
13 papers · 1 filter
Efficient Federated Prompt Tuning for Black-box Large Pre-trained Models
Zihao Lin, Yan Sun, Yifan Shi +4
With the blowout development of pre-trained models (PTMs), the efficient tuning of these models for diverse downstream applications has emerged as a pivotal research concern. Altho…
Towards Understanding Generalization and Stability Gaps between Centralized and Decentralized Federated Learning
Yan Sun, Li Shen, Dacheng Tao
As two mainstream frameworks in federated learning (FL), both centralized and decentralized approaches have shown great application value in practical scenarios. However, existing…
Temporal Sentence Grounding in Streaming Videos
Tian Gan, Xiao Wang, Yan Sun +3
This paper aims to tackle a novel task - Temporal Sentence Grounding in Streaming Videos (TSGSV). The goal of TSGSV is to evaluate the relevance between a video stream and a given…
Efficient Federated Learning via Local Adaptive Amended Optimizer with Linear Speedup
Yan Sun, Li Shen, Hao Sun +2
Adaptive optimization has achieved notable success for distributed learning while extending adaptive optimizer to federated Learning (FL) suffers from severe inefficiency, includin…
Understanding How Consistency Works in Federated Learning via Stage-wise Relaxed Initialization
Yan Sun, Li Shen, Dacheng Tao
Federated learning (FL) is a distributed paradigm that coordinates massive local clients to collaboratively train a global model via stage-wise local training processes on the hete…
Towards More Suitable Personalization in Federated Learning via Decentralized Partial Model Training
Yifan Shi, Yingqi Liu, Yan Sun +4
Personalized federated learning (PFL) aims to produce the greatest personalized model for each client to face an insurmountable problem--data heterogeneity in real FL systems. Howe…
Dynamic Regularized Sharpness Aware Minimization in Federated Learning: Approaching Global Consistency and Smooth Landscape
Yan Sun, Li Shen, Shixiang Chen +2
In federated learning (FL), a cluster of local clients are chaired under the coordination of the global server and cooperatively train one model with privacy protection. Due to the…
On Efficient Training of Large-Scale Deep Learning Models: A Literature Review
Li Shen, Yan Sun, Zhiyuan Yu +3
The field of deep learning has witnessed significant progress, particularly in computer vision (CV), natural language processing (NLP), and speech. The use of large-scale models tr…
Visual Prompt Based Personalized Federated Learning
Guanghao Li, Wansen Wu, Yan Sun +3
As a popular paradigm of distributed learning, personalized federated learning (PFL) allows personalized models to improve generalization ability and robustness by utilizing knowle…
Subspace based Federated Unlearning
Guanghao Li, Li Shen, Yan Sun +3
Federated learning (FL) enables multiple clients to train a machine learning model collaboratively without exchanging their local data. Federated unlearning is an inverse FL proces…
Fusion of Global and Local Knowledge for Personalized Federated Learning
Tiansheng Huang, Li Shen, Yan Sun +2
Personalized federated learning, as a variant of federated learning, trains customized models for clients using their heterogeneously distributed data. However, it is still inconcl…
FedSpeed: Larger Local Interval, Less Communication Round, and Higher Generalization Accuracy
Yan Sun, Li Shen, Tiansheng Huang +2
Federated learning is an emerging distributed machine learning framework which jointly trains a global model via a large number of local devices with data privacy protections. Its…
Improving the Model Consistency of Decentralized Federated Learning
Yifan Shi, Li Shen, Kang Wei +4
To mitigate the privacy leakages and communication burdens of Federated Learning (FL), decentralized FL (DFL) discards the central server and each client only communicates with its…