623 citations · 780 across the 21 of their papers we have counts for
21 papers
Towards Attack-tolerant Federated Learning via Critical Parameter Analysis
Sungwon Han, Sungwon Park, Fangzhao Wu +4
Federated learning is used to train a shared model in a decentralized way without clients sharing private data with each other. Federated learning systems are susceptible to poison…
FedDefender: Client-Side Attack-Tolerant Federated Learning
Sungwon Park, Sungwon Han, Fangzhao Wu +4
Federated learning enables learning from decentralized data sources without compromising privacy, which makes it a crucial technique. However, it is vulnerable to model poisoning a…
FedSampling: A Better Sampling Strategy for Federated Learning
Tao Qi, Fangzhao Wu, Lingjuan Lyu +2
Federated learning (FL) is an important technique for learning models from decentralized data in a privacy-preserving way. Existing FL methods usually uniformly sample clients for…
Are You Copying My Model? Protecting the Copyright of Large Language Models for EaaS via Backdoor Watermark
Wenjun Peng, Jingwei Yi, Fangzhao Wu +7
Large language models (LLMs) have demonstrated powerful capabilities in both text understanding and generation. Companies have begun to offer Embedding as a Service (EaaS) based on…
DualFair: Fair Representation Learning at Both Group and Individual Levels via Contrastive Self-supervision
Sungwon Han, Seungeon Lee, Fangzhao Wu +5
Algorithmic fairness has become an important machine learning problem, especially for mission-critical Web applications. This work presents a self-supervised model, called DualFair…
Robust Federated Learning against both Data Heterogeneity and Poisoning Attack via Aggregation Optimization
Yueqi Xie, Weizhong Zhang, Renjie Pi +4
Non-IID data distribution across clients and poisoning attacks are two main challenges in real-world federated learning (FL) systems. While both of them have attracted great resear…
Effective and Efficient Query-aware Snippet Extraction for Web Search
Jingwei Yi, Fangzhao Wu, Chuhan Wu +4
Query-aware webpage snippet extraction is widely used in search engines to help users better understand the content of the returned webpages before clicking. Although important, it…
FedX: Unsupervised Federated Learning with Cross Knowledge Distillation
Sungwon Han, Sungwon Park, Fangzhao Wu +4
This paper presents FedX, an unsupervised federated learning framework. Our model learns unbiased representation from decentralized and heterogeneous local data. It employs a two-s…
Two-Stage Neural Contextual Bandits for Personalised News Recommendation
Mengyan Zhang, Thanh Nguyen-Tang, Fangzhao Wu +3
We consider the problem of personalised news recommendation where each user consumes news in a sequential fashion. Existing personalised news recommendation methods focus on exploi…
MetaFed: Federated Learning among Federations with Cyclic Knowledge Distillation for Personalized Healthcare
Yiqiang Chen, Wang Lu, Xin Qin +2
Federated learning has attracted increasing attention to building models without accessing the raw user data, especially in healthcare. In real applications, different federations…
FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learning
Tao Qi, Fangzhao Wu, Chuhan Wu +5
Vertical federated learning (VFL) is a privacy-preserving machine learning paradigm that can learn models from features distributed on different platforms in a privacy-preserving w…
Robust Quantity-Aware Aggregation for Federated Learning
Jingwei Yi, Fangzhao Wu, Huishuai Zhang +4
Federated learning (FL) enables multiple clients to collaboratively train models without sharing their local data, and becomes an important privacy-preserving machine learning fram…
FedCL: Federated Contrastive Learning for Privacy-Preserving Recommendation
Chuhan Wu, Fangzhao Wu, Tao Qi +2
Contrastive learning is widely used for recommendation model learning, where selecting representative and informative negative samples is critical. Existing methods usually focus o…
ProFairRec: Provider Fairness-aware News Recommendation
Tao Qi, Fangzhao Wu, Chuhan Wu +5
News recommendation aims to help online news platform users find their preferred news articles. Existing news recommendation methods usually learn models from historical user behav…
NoisyTune: A Little Noise Can Help You Finetune Pretrained Language Models Better
Chuhan Wu, Fangzhao Wu, Tao Qi +2
Effectively finetuning pretrained language models (PLMs) is critical for their success in downstream tasks. However, PLMs may have risks in overfitting the pretraining tasks and da…
FedAttack: Effective and Covert Poisoning Attack on Federated Recommendation via Hard Sampling
Chuhan Wu, Fangzhao Wu, Tao Qi +2
Federated learning (FL) is a feasible technique to learn personalized recommendation models from decentralized user data. Unfortunately, federated recommender systems are vulnerabl…
No One Left Behind: Inclusive Federated Learning over Heterogeneous Devices
Ruixuan Liu, Fangzhao Wu, Chuhan Wu +4
Federated learning (FL) is an important paradigm for training global models from decentralized data in a privacy-preserving way. Existing FL methods usually assume the global model…
UA-FedRec: Untargeted Attack on Federated News Recommendation
Jingwei Yi, Fangzhao Wu, Bin Zhu +4
News recommendation is critical for personalized news distribution. Federated news recommendation enables collaborative model learning from many clients without sharing their raw d…
Game of Privacy: Towards Better Federated Platform Collaboration under Privacy Restriction
Chuhan Wu, Fangzhao Wu, Tao Qi +4
Vertical federated learning (VFL) aims to train models from cross-silo data with different feature spaces stored on different platforms. Existing VFL methods usually assume all dat…
FedKD: Communication Efficient Federated Learning via Knowledge Distillation
Chuhan Wu, Fangzhao Wu, Lingjuan Lyu +2
Federated learning is widely used to learn intelligent models from decentralized data. In federated learning, clients need to communicate their local model updates in each iteratio…
Personalized News Recommendation: Methods and Challenges
Chuhan Wu, Fangzhao Wu, Yongfeng Huang +1
Personalized news recommendation is an important technique to help users find their interested news information and alleviate their information overload. It has been extensively st…