3 citations · 10 across the 12 of their papers we have counts for
12 papers
PerFedRec++: Enhancing Personalized Federated Recommendation with Self-Supervised Pre-Training
Sichun Luo, Yuanzhang Xiao, Xinyi Zhang +3
Federated recommendation systems employ federated learning techniques to safeguard user privacy by transmitting model parameters instead of raw user data between user devices and t…
Killing Two Birds with One Stone: Quantization Achieves Privacy in Distributed Learning
Guangfeng Yan, Tan Li, Kui Wu +1
Communication efficiency and privacy protection are two critical issues in distributed machine learning. Existing methods tackle these two issues separately and may have a high imp…
Adaptive Top-K in SGD for Communication-Efficient Distributed Learning
Mengzhe Ruan, Guangfeng Yan, Yuanzhang Xiao +2
Distributed stochastic gradient descent (SGD) with gradient compression has become a popular communication-efficient solution for accelerating distributed learning. One commonly us…
Towards Communication Efficient and Fair Federated Personalized Sequential Recommendation
Sichun Luo, Yuanzhang Xiao, Yang Liu +2
Federated recommendations leverage the federated learning (FL) techniques to make privacy-preserving recommendations. Though recent success in the federated recommender system, sev…
Towards Efficient Communications in Federated Learning: A Contemporary Survey
Zihao Zhao, Yuzhu Mao, Yang Liu +4
In the traditional distributed machine learning scenario, the user's private data is transmitted between clients and a central server, which results in significant potential privac…
Privacy-Preserving Communication-Efficient Federated Multi-Armed Bandits
Tan Li, Linqi Song
Communication bottleneck and data privacy are two critical concerns in federated multi-armed bandit (MAB) problems, such as situations in decision-making and recommendations of con…
CSAGN: Conversational Structure Aware Graph Network for Conversational Semantic Role Labeling
Han Wu, Kun Xu, Linqi Song
Conversational semantic role labeling (CSRL) is believed to be a crucial step towards dialogue understanding. However, it remains a major challenge for existing CSRL parser to hand…
Byzantine-robust Federated Learning through Collaborative Malicious Gradient Filtering
Jian Xu, Shao-Lun Huang, Linqi Song +1
Gradient-based training in federated learning is known to be vulnerable to faulty/malicious clients, which are often modeled as Byzantine clients. To this end, previous work either…
DQ-SGD: Dynamic Quantization in SGD for Communication-Efficient Distributed Learning
Guangfeng Yan, Shao-Lun Huang, Tian Lan +1
Gradient quantization is an emerging technique in reducing communication costs in distributed learning. Existing gradient quantization algorithms often rely on engineering heuristi…
Domain-Adaptive Pretraining Methods for Dialogue Understanding
Han Wu, Kun Xu, Linfeng Song +3
Language models like BERT and SpanBERT pretrained on open-domain data have obtained impressive gains on various NLP tasks. In this paper, we probe the effectiveness of domain-adapt…
Coded Alternating Least Squares for Straggler Mitigation in Distributed Recommendations
Siyuan Wang, Qifa Yan, Jingjing Zhang +2
Matrix factorization is an important representation learning algorithm, e.g., recommender systems, where a large matrix can be factorized into the product of two low dimensional ma…
Conversational Semantic Role Labeling
Kun Xu, Han Wu, Linfeng Song +3
Semantic role labeling (SRL) aims to extract the arguments for each predicate in an input sentence. Traditional SRL can fail to analyze dialogues because it only works on every sin…