71 citations · 97 across the 9 of their papers we have counts for
9 papers
GLOCALFAIR: Jointly Improving Global and Local Group Fairness in Federated Learning
Syed Irfan Ali Meerza, Luyang Liu, Jiaxin Zhang +1
Federated learning (FL) has emerged as a prospective solution for collaboratively learning a shared model across clients without sacrificing their data privacy. However, the federa…
SAC3: Reliable Hallucination Detection in Black-Box Language Models via Semantic-aware Cross-check Consistency
Jiaxin Zhang, Zhuohang Li, Kamalika Das +2
Hallucination detection is a critical step toward understanding the trustworthiness of modern language models (LMs). To achieve this goal, we re-examine existing detection approach…
Interactive Multi-fidelity Learning for Cost-effective Adaptation of Language Model with Sparse Human Supervision
Jiaxin Zhang, Zhuohang Li, Kamalika Das +1
Large language models (LLMs) have demonstrated remarkable capabilities in various tasks. However, their suitability for domain-specific tasks, is limited due to their immense scale…
RecUP-FL: Reconciling Utility and Privacy in Federated Learning via User-configurable Privacy Defense
Yue Cui, Syed Irfan Ali Meerza, Zhuohang Li +3
Federated learning (FL) provides a variety of privacy advantages by allowing clients to collaboratively train a model without sharing their private data. However, recent studies ha…
Speech Privacy Leakage from Shared Gradients in Distributed Learning
Zhuohang Li, Jiaxin Zhang, Jian Liu
Distributed machine learning paradigms, such as federated learning, have been recently adopted in many privacy-critical applications for speech analysis. However, such frameworks a…
Auditing Privacy Defenses in Federated Learning via Generative Gradient Leakage
Zhuohang Li, Jiaxin Zhang, Luyang Liu +1
Federated Learning (FL) framework brings privacy benefits to distributed learning systems by allowing multiple clients to participate in a learning task under the coordination of a…
Simulation Intelligence: Towards a New Generation of Scientific Methods
Alexander Lavin, David Krakauer, Hector Zenil +21
The original "Seven Motifs" set forth a roadmap of essential methods for the field of scientific computing, where a motif is an algorithmic method that captures a pattern of comput…
Byzantine-robust Federated Learning through Spatial-temporal Analysis of Local Model Updates
Zhuohang Li, Luyang Liu, Jiaxin Zhang +1
Federated Learning (FL) enables multiple distributed clients (e.g., mobile devices) to collaboratively train a centralized model while keeping the training data locally on the clie…
Power-Law Graph Cuts
Xiangyang Zhou, Jiaxin Zhang, Brian Kulis
Algorithms based on spectral graph cut objectives such as normalized cuts, ratio cuts and ratio association have become popular in recent years because they are widely applicable a…