22 citations · 38 across the 7 of their papers we have counts for
8 papers
One-Shot Federated Model Editing for Device Dynamics at the Edge
Youming Tao, Weijun Yin, Huanyi Xie +5
Standard federated learning (FL) requires persistent device connectivity across multiple communication rounds, making it unsuitable for dynamic environments such as vehicular netwo…
Federated Fine-Tuning of Sparsely-Activated Large Language Models on Resource-Constrained Devices
Fahao Chen, Jie Wan, Peng Li +2
Federated fine-tuning of Mixture-of-Experts (MoE)-based large language models (LLMs) is challenging due to their massive computational requirements and the resource constraints of…
DistrAttention: An Efficient and Flexible Self-Attention Mechanism on Modern GPUs
Haolin Jin, Mengbai Xiao, Yuan Yuan +4
The Transformer architecture has revolutionized deep learning, delivering the state-of-the-art performance in areas such as natural language processing, computer vision, and time s…
Federating to Grow Transformers with Constrained Resources without Model Sharing
Shikun Shen, Yifei Zou, Yuan Yuan +4
The high resource consumption of large-scale models discourages resource-constrained users from developing their customized transformers. To this end, this paper considers a federa…
A Resource-Adaptive Approach for Federated Learning under Resource-Constrained Environments
Ruirui Zhang, Xingze Wu, Yifei Zou +4
The paper studies a fundamental federated learning (FL) problem involving multiple clients with heterogeneous constrained resources. Compared with the numerous training parameters,…
BFT-DSN: A Byzantine Fault Tolerant Decentralized Storage Network
Hechuan Guo, Minghui Xu, Jiahao Zhang +4
With the rapid development of blockchain and its applications, the amount of data stored on decentralized storage networks (DSNs) has grown exponentially. DSNs bring together affor…
Communication Efficient and Provable Federated Unlearning
Youming Tao, Cheng-Long Wang, Miao Pan +3
We study federated unlearning, a novel problem to eliminate the impact of specific clients or data points on the global model learned via federated learning (FL). This problem is d…
FedRFQ: Prototype-Based Federated Learning with Reduced Redundancy, Minimal Failure, and Enhanced Quality
Biwei Yan, Hongliang Zhang, Minghui Xu +2
Federated learning is a powerful technique that enables collaborative learning among different clients. Prototype-based federated learning is a specific approach that improves the…