4 citations · 13 across the 37 of their papers we have counts for
13 papers · 1 filter
Benchmarking Composable Compression Techniques in Mixture-of-Experts LLMs
Afsara Benazir, Chen Chen, Rongxiao Qu +3
Mixture-of-Experts (MoE) LLMs scale model capacity efficiently through sparse activation, but their large expert parameter footprint, routing imbalance, and long-context KV-cache g…
FEDEXCHANGE: Bridging the Domain Gap in Federated Object Detection for Free
Haolin Yuan, Jingtao Li, Weiming Zhuang +2
Federated Object Detection (FOD) enables clients to collaboratively train a global object detection model without accessing their local data from diverse domains. However, signific…
Closer to Reality: Practical Semi-Supervised Federated Learning for Foundation Model Adaptation
Guangyu Sun, Jingtao Li, Weiming Zhuang +2
Foundation models (FMs) exhibit remarkable generalization but require adaptation to downstream tasks, particularly in privacy-sensitive applications. Due to data privacy regulation…
Unlearning through Knowledge Overwriting: Reversible Federated Unlearning via Selective Sparse Adapter
Zhengyi Zhong, Weidong Bao, Ji Wang +4
Federated Learning is a promising paradigm for privacy-preserving collaborative model training. In practice, it is essential not only to continuously train the model to acquire new…
Exploit Gradient Skewness to Circumvent Byzantine Defenses for Federated Learning
Yuchen Liu, Chen Chen, Lingjuan Lyu +2
Federated Learning (FL) is notorious for its vulnerability to Byzantine attacks. Most current Byzantine defenses share a common inductive bias: among all the gradients, the densely…
Copyright-Aware Incentive Scheme for Generative Art Models Using Hierarchical Reinforcement Learning
Zhuan Shi, Yifei Song, Xiaoli Tang +2
Generative art using Diffusion models has achieved remarkable performance in image generation and text-to-image tasks. However, the increasing demand for training data in generativ…
Self-Comparison for Dataset-Level Membership Inference in Large (Vision-)Language Models
Jie Ren, Kangrui Chen, Chen Chen +4
Large Language Models (LLMs) and Vision-Language Models (VLMs) have made significant advancements in a wide range of natural language processing and vision-language tasks. Access t…
FLoRA: Federated Fine-Tuning Large Language Models with Heterogeneous Low-Rank Adaptations
Ziyao Wang, Zheyu Shen, Yexiao He +4
The rapid development of Large Language Models (LLMs) has been pivotal in advancing AI, with pre-trained LLMs being adaptable to diverse downstream tasks through fine-tuning. Feder…
Towards Fundamentally Scalable Model Selection: Asymptotically Fast Update and Selection
Wenxiao Wang, Weiming Zhuang, Lingjuan Lyu
The advancement of deep learning technologies is bringing new models every day, motivating the study of scalable model selection. An ideal model selection scheme should minimally s…
Federated Learning under Partially Class-Disjoint Data via Manifold Reshaping
Ziqing Fan, Jiangchao Yao, Ruipeng Zhang +3
Statistical heterogeneity severely limits the performance of federated learning (FL), motivating several explorations e.g., FedProx, MOON and FedDyn, to alleviate this problem. Des…
FedSAC: Dynamic Submodel Allocation for Collaborative Fairness in Federated Learning
Zihui Wang, Zheng Wang, Lingjuan Lyu +6
Collaborative fairness stands as an essential element in federated learning to encourage client participation by equitably distributing rewards based on individual contributions. E…
FedP3: Federated Personalized and Privacy-friendly Network Pruning under Model Heterogeneity
Kai Yi, Nidham Gazagnadou, Peter Richtárik +1
The interest in federated learning has surged in recent research due to its unique ability to train a global model using privacy-secured information held locally on each client. Th…
FedMef: Towards Memory-efficient Federated Dynamic Pruning
Hong Huang, Weiming Zhuang, Chen Chen +1
Federated learning (FL) promotes decentralized training while prioritizing data confidentiality. However, its application on resource-constrained devices is challenging due to the…