22 citations · 48 across the 18 of their papers we have counts for
19 papers
Queue-Aware Graph Reinforcement Learning for UAV-ISAC-Assisted Maritime Data Collection
Bohan Li, Min Ye, Haochen Liu +5
This paper studies high-altitude platform (HAP)-assisted sparse cooperative integrated sensing and communication (ISAC) for UAV-enabled ocean monitoring. A fleet of rotary-wing UAV…
Distributed Bilevel Optimization with Dual Pruning for Resource-limited Clients
Mingyi Li, Xiao Zhang, Ruisheng Zheng +4
With the development of large-scale models, traditional distributed bilevel optimization algorithms cannot be applied directly in low-resource clients. The key reason lies in the e…
Second-Order Convergence in Private Stochastic Non-Convex Optimization
Youming Tao, Zuyuan Zhang, Dongxiao Yu +3
We investigate the problem of finding second-order stationary points (SOSP) in differentially private (DP) stochastic non-convex optimization. Existing methods suffer from two key…
Amplified Vulnerabilities: Structured Jailbreak Attacks on LLM-based Multi-Agent Debate
Senmao Qi, Yifei Zou, Peng Li +3
Multi-Agent Debate (MAD), leveraging collaborative interactions among Large Language Models (LLMs), aim to enhance reasoning capabilities in complex tasks. However, the security im…
t-READi: Transformer-Powered Robust and Efficient Multimodal Inference for Autonomous Driving
Pengfei Hu, Yuhang Qian, Tianyue Zheng +5
Given the wide adoption of multimodal sensors (e.g., camera, lidar, radar) by autonomous vehicles (AVs), deep analytics to fuse their outputs for a robust perception become imperat…
PDSR: A Privacy-Preserving Diversified Service Recommendation Method on Distributed Data
Lina Wang, Huan Yang, Yiran Shen +4
The last decade has witnessed a tremendous growth of service computing, while efficient service recommendation methods are desired to recommend high-quality services to users. It i…
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,…
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…
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…
Spatial Crowdsourcing Task Allocation Scheme for Massive Data with Spatial Heterogeneity
Kun Li, Shengling Wang, Hongwei Shi +2
Spatial crowdsourcing (SC) engages large worker pools for location-based tasks, attracting growing research interest. However, prior SC task allocation approaches exhibit limitatio…
An Adaptive and Modular Blockchain Enabled Architecture for a Decentralized Metaverse
Ye Cheng, Yihao Guo, Minghui Xu +3
A metaverse breaks the boundaries of time and space between people, realizing a more realistic virtual experience, improving work efficiency, and creating a new business model. Blo…
A Self-Correcting Sequential Recommender
Yujie Lin, Chenyang Wang, Zhumin Chen +6
Sequential recommendations aim to capture users' preferences from their historical interactions so as to predict the next item that they will interact with. Sequential recommendati…
Exploring Blockchain Technology through a Modular Lens: A Survey
Minghui Xu, Yihao Guo, Chunchi Liu +5
Blockchain has attracted significant attention in recent years due to its potential to revolutionize various industries by providing trustlessness. To comprehensively examine block…
Byzantine-Resilient Federated Learning at Edge
Youming Tao, Sijia Cui, Wenlu Xu +4
Both Byzantine resilience and communication efficiency have attracted tremendous attention recently for their significance in edge federated learning. However, most existing algori…
Collaborative Learning in General Graphs with Limited Memorization: Complexity, Learnability, and Reliability
Feng Li, Xuyang Yuan, Lina Wang +4
We consider a K-armed bandit problem in general graphs where agents are arbitrarily connected and each of them has limited memorizing capabilities and communication bandwidth. The…
An Uncertainty- and Collusion-Proof Voting Consensus Mechanism in Blockchain
Shengling Wang, Xidi Qu, Qin Hu +1
Though voting-based consensus algorithms in Blockchain outperform proof-based ones in energy- and transaction-efficiency, they are prone to incur wrong elections and bribery electi…