64 citations · 80 across the 16 of their papers we have counts for
12 papers · 1 filter
Graph-Reward-SQL: Execution-Free Reinforcement Learning for Text-to-SQL via Graph Matching and Stepwise Reward
Han Weng, Puzhen Wu, Longjie Cui +10
Reinforcement learning (RL) has been widely adopted to enhance the performance of large language models (LLMs) on Text-to-SQL tasks. However, existing methods often rely on executi…
Understanding Generalization of Federated Learning: the Trade-off between Model Stability and Optimization
Dun Zeng, Zheshun Wu, Shiyu Liu +3
Federated Learning (FL) is a distributed learning approach that trains machine learning models across multiple devices while keeping their local data private. However, FL often fac…
Topology Learning for Heterogeneous Decentralized Federated Learning over Unreliable D2D Networks
Zheshun Wu, Zenglin Xu, Dun Zeng +2
With the proliferation of intelligent mobile devices in wireless device-to-device (D2D) networks, decentralized federated learning (DFL) has attracted significant interest. Compare…
Federated Knowledge Graph Completion via Latent Embedding Sharing and Tensor Factorization
Maolin Wang, Dun Zeng, Zenglin Xu +2
Knowledge graphs (KGs), which consist of triples, are inherently incomplete and always require completion procedure to predict missing triples. In real-world scenarios, KGs are dis…
Advocating for the Silent: Enhancing Federated Generalization for Non-Participating Clients
Zheshun Wu, Zenglin Xu, Dun Zeng +2
Federated Learning (FL) has surged in prominence due to its capability of collaborative model training without direct data sharing. However, the vast disparity in local data distri…
On the Power of Adaptive Weighted Aggregation in Heterogeneous Federated Learning and Beyond
Dun Zeng, Zenglin Xu, Shiyu Liu +3
Federated averaging (FedAvg) is the most fundamental algorithm in Federated learning (FL). Previous theoretical results assert that FedAvg convergence and generalization degenerate…
Enhanced Federated Optimization: Adaptive Unbiased Client Sampling with Reduced Variance
Dun Zeng, Zenglin Xu, Yu Pan +3
Federated Learning (FL) is a distributed learning paradigm to train a global model across multiple devices without collecting local data. In FL, a server typically selects a subset…
Personalized Federated Learning via Amortized Bayesian Meta-Learning
Shiyu Liu, Shaogao Lv, Dun Zeng +3
Federated learning is a decentralized and privacy-preserving technique that enables multiple clients to collaborate with a server to learn a global model without exposing their pri…
FedNoisy: Federated Noisy Label Learning Benchmark
Siqi Liang, Jintao Huang, Junyuan Hong +3
Federated learning has gained popularity for distributed learning without aggregating sensitive data from clients. But meanwhile, the distributed and isolated nature of data isolat…
StoCFL: A Stochastically Clustered Federated Learning Framework for Non-IID Data with Dynamic Client Participation
Dun Zeng, Xiangjing Hu, Shiyu Liu +3
Federated learning is a distributed learning framework that takes full advantage of private data samples kept on edge devices. In real-world federated learning systems, these data…
A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacy
Yifei Zhang, Dun Zeng, Jinglong Luo +2
Trustworthy artificial intelligence (AI) technology has revolutionized daily life and greatly benefited human society. Among various AI technologies, Federated Learning (FL) stands…
FedLab: A Flexible Federated Learning Framework
Dun Zeng, Siqi Liang, Xiangjing Hu +2
Federated learning (FL) is a machine learning field in which researchers try to facilitate model learning process among multiparty without violating privacy protection regulations.…