activity
20192025
most citedA constant FPT approximation algorithm for hard-capacitated k-means

3 citations · 8 across the 17 of their papers we have counts for

collaborators
Showing 2024Show all

7 papers · 1 filter

cs.NI2024★ 1 cited

Partially Synchronous BFT Consensus Made Practical in Wireless Networks

Shuo Liu, Minghui Xu, Yuezhou Zheng +4

Consensus is becoming increasingly important in wireless networks. Partially synchronous BFT consensus, a significant branch of consensus, has made considerable progress in wired n…

cs.DC2024

Unity is Power: Semi-Asynchronous Collaborative Training of Large-Scale Models with Structured Pruning in Resource-Limited Clients

Yan Li, Xiao Zhang, Mingyi Li +7

In this work, we study to release the potential of massive heterogeneous weak computing power to collaboratively train large-scale models on dispersed datasets. In order to improve…

cs.AI2024

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…

cs.LG2024★ 1 cited

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,…

cs.CV2024

Leveraging Unknown Objects to Construct Labeled-Unlabeled Meta-Relationships for Zero-Shot Object Navigation

Yanwei Zheng, Changrui Li, Chuanlin Lan +5

Zero-shot object navigation (ZSON) addresses situation where an agent navigates to an unseen object that does not present in the training set. Previous works mainly train agent usi…

cs.LG2024

Cooperative Backdoor Attack in Decentralized Reinforcement Learning with Theoretical Guarantee

Mengtong Gao, Yifei Zou, Zuyuan Zhang +2

The safety of decentralized reinforcement learning (RL) is a challenging problem since malicious agents can share their poisoned policies with benign agents. The paper investigates…