60 citations · 115 across the 7 of their papers we have counts for
7 papers
Learning Cautiously in Federated Learning with Noisy and Heterogeneous Clients
Chenrui Wu, Zexi Li, Fangxin Wang +1
Federated learning (FL) is a distributed framework for collaboratively training with privacy guarantees. In real-world scenarios, clients may have Non-IID data (local class imbalan…
Learning to Backdoor Federated Learning
Henger Li, Chen Wu, Sencun Zhu +1
In a federated learning (FL) system, malicious participants can easily embed backdoors into the aggregated model while maintaining the model's performance on the main task. To this…
Certified Robustness to Word Substitution Ranking Attack for Neural Ranking Models
Chen Wu, Ruqing Zhang, Jiafeng Guo +4
Neural ranking models (NRMs) have achieved promising results in information retrieval. NRMs have also been shown to be vulnerable to adversarial examples. A typical Word Substituti…
Learning from Drivers to Tackle the Amazon Last Mile Routing Research Challenge
Chen Wu, Yin Song, Verdi March +1
The goal of the Amazon Last Mile Routing Research Challenge is to integrate the real-life experience of Amazon drivers into the solution of optimal route planning and optimization.…
PRADA: Practical Black-Box Adversarial Attacks against Neural Ranking Models
Chen Wu, Ruqing Zhang, Jiafeng Guo +3
Neural ranking models (NRMs) have shown remarkable success in recent years, especially with pre-trained language models. However, deep neural models are notorious for their vulnera…
Federated Unlearning with Knowledge Distillation
Chen Wu, Sencun Zhu, Prasenjit Mitra
Federated Learning (FL) is designed to protect the data privacy of each client during the training process by transmitting only models instead of the original data. However, the tr…