195 citations · 209 across the 17 of their papers we have counts for
13 papers · 1 filter
Learning to Optimize Job Shop Scheduling Under Structural Uncertainty
Rui Zhang, Jianwei Niu, Xuefeng Liu +2
The Job-Shop Scheduling Problem (JSSP), under various forms of manufacturing uncertainty, has recently attracted considerable research attention. Most existing studies focus on par…
Can Federated Learning Safeguard Private Data in LLM Training? Vulnerabilities, Attacks, and Defense Evaluation
Wenkai Guo, Xuefeng Liu, Haolin Wang +3
Fine-tuning large language models (LLMs) with local data is a widely adopted approach for organizations seeking to adapt LLMs to their specific domains. Given the shared characteri…
Enhancing Visual Representation with Textual Semantics: Textual Semantics-Powered Prototypes for Heterogeneous Federated Learning
Xinghao Wu, Jianwei Niu, Xuefeng Liu +4
Federated Prototype Learning (FedPL) has emerged as an effective strategy for handling data heterogeneity in Federated Learning (FL). In FedPL, clients collaboratively construct a…
The Other Side of the Coin: Unveiling the Downsides of Model Aggregation in Federated Learning from a Layer-peeled Perspective
Guogang Zhu, Xuefeng Liu, Jianwei Niu +2
It is often observed that the aggregated model in FL underperforms on local data until after several rounds of local training. This temporary performance drop can potentially slow…
Why Go Full? Elevating Federated Learning Through Partial Network Updates
Haolin Wang, Xuefeng Liu, Jianwei Niu +2
Federated learning is a distributed machine learning paradigm designed to protect user data privacy, which has been successfully implemented across various scenarios. In traditiona…
Tackling Feature-Classifier Mismatch in Federated Learning via Prompt-Driven Feature Transformation
Xinghao Wu, Jianwei Niu, Xuefeng Liu +4
Federated Learning (FL) faces challenges due to data heterogeneity, which limits the global model's performance across diverse client distributions. Personalized Federated Learning…
The Diversity Bonus: Learning from Dissimilar Distributed Clients in Personalized Federated Learning
Xinghao Wu, Xuefeng Liu, Jianwei Niu +4
Personalized Federated Learning (PFL) is a commonly used framework that allows clients to collaboratively train their personalized models. PFL is particularly useful for handling s…
DualFed: Enjoying both Generalization and Personalization in Federated Learning via Hierachical Representations
Guogang Zhu, Xuefeng Liu, Jianwei Niu +3
In personalized federated learning (PFL), it is widely recognized that achieving both high model generalization and effective personalization poses a significant challenge due to t…
Decoupling General and Personalized Knowledge in Federated Learning via Additive and Low-Rank Decomposition
Xinghao Wu, Xuefeng Liu, Jianwei Niu +4
To address data heterogeneity, the key strategy of Personalized Federated Learning (PFL) is to decouple general knowledge (shared among clients) and client-specific knowledge, as t…
Estimating before Debiasing: A Bayesian Approach to Detaching Prior Bias in Federated Semi-Supervised Learning
Guogang Zhu, Xuefeng Liu, Xinghao Wu +4
Federated Semi-Supervised Learning (FSSL) leverages both labeled and unlabeled data on clients to collaboratively train a model.In FSSL, the heterogeneous data can introduce predic…
Bold but Cautious: Unlocking the Potential of Personalized Federated Learning through Cautiously Aggressive Collaboration
Xinghao Wu, Xuefeng Liu, Jianwei Niu +2
Personalized federated learning (PFL) reduces the impact of non-independent and identically distributed (non-IID) data among clients by allowing each client to train a personalized…
Take Your Pick: Enabling Effective Personalized Federated Learning within Low-dimensional Feature Space
Guogang Zhu, Xuefeng Liu, Shaojie Tang +3
Personalized federated learning (PFL) is a popular framework that allows clients to have different models to address application scenarios where clients' data are in different doma…
Unlocking the Potential of Federated Learning for Deeper Models
Haolin Wang, Xuefeng Liu, Jianwei Niu +2
Federated learning (FL) is a new paradigm for distributed machine learning that allows a global model to be trained across multiple clients without compromising their privacy. Alth…