60 citations · 207 across the 20 of their papers we have counts for
20 papers
A Survey for Federated Learning Evaluations: Goals and Measures
Di Chai, Leye Wang, Liu Yang +3
Evaluation is a systematic approach to assessing how well a system achieves its intended purpose. Federated learning (FL) is a novel paradigm for privacy-preserving machine learnin…
Theoretically Principled Federated Learning for Balancing Privacy and Utility
Xiaojin Zhang, Wenjie Li, Yiming Li +3
We propose a general learning framework for the protection mechanisms that protects privacy via distorting model parameters, which facilitates the trade-off between privacy and uti…
Towards Achieving Near-optimal Utility for Privacy-Preserving Federated Learning via Data Generation and Parameter Distortion
Xiaojin Zhang, Kai Chen, Qiang Yang
Federated learning (FL) enables participating parties to collaboratively build a global model with boosted utility without disclosing private data information. Appropriate protecti…
Federated Learning without Full Labels: A Survey
Yilun Jin, Yang Liu, Kai Chen +1
Data privacy has become an increasingly important concern in real-world big data applications such as machine learning. To address the problem, federated learning (FL) has been a p…
A Survey on Heterogeneous Federated Learning
Dashan Gao, Xin Yao, Qiang Yang
Federated learning (FL) has been proposed to protect data privacy and virtually assemble the isolated data silos by cooperatively training models among organizations without breach…
Regularized Data Programming with Automated Bayesian Prior Selection
Jacqueline R. M. A. Maasch, Hao Zhang, Qian Yang +2
The cost of manual data labeling can be a significant obstacle in supervised learning. Data programming (DP) offers a weakly supervised solution for training dataset creation, wher…
Cross-domain Cross-architecture Black-box Attacks on Fine-tuned Models with Transferred Evolutionary Strategies
Yinghua Zhang, Yangqiu Song, Kun Bai +1
Fine-tuning can be vulnerable to adversarial attacks. Existing works about black-box attacks on fine-tuned models (BAFT) are limited by strong assumptions. To fill the gap, we prop…
Practical Active Learning with Model Selection for Small Data
Maryam Pardakhti, Nila Mandal, Anson W. K. Ma +1
Active learning is of great interest for many practical applications, especially in industry and the physical sciences, where there is a strong need to minimize the number of costl…
Practical Lossless Federated Singular Vector Decomposition over Billion-Scale Data
Di Chai, Leye Wang, Junxue Zhang +4
With the enactment of privacy-preserving regulations, e.g., GDPR, federated SVD is proposed to enable SVD-based applications over different data sources without revealing the origi…
FedEval: A Holistic Evaluation Framework for Federated Learning
Di Chai, Leye Wang, Liu Yang +3
Federated Learning (FL) has been widely accepted as the solution for privacy-preserving machine learning without collecting raw data. While new technologies proposed in the past fe…
Improving Text Generation with Student-Forcing Optimal Transport
Guoyin Wang, Chunyuan Li, Jianqiao Li +10
Neural language models are often trained with maximum likelihood estimation (MLE), where the next word is generated conditioned on the ground-truth word tokens. During testing, how…
Faster On-Device Training Using New Federated Momentum Algorithm
Zhouyuan Huo, Qian Yang, Bin Gu +1
Mobile crowdsensing has gained significant attention in recent years and has become a critical paradigm for emerging Internet of Things applications. The sensing devices continuous…
Graph-Driven Generative Models for Heterogeneous Multi-Task Learning
Wenlin Wang, Hongteng Xu, Zhe Gan +6
We propose a novel graph-driven generative model, that unifies multiple heterogeneous learning tasks into the same framework. The proposed model is based on the fact that heterogen…
Improving Textual Network Learning with Variational Homophilic Embeddings
Wenlin Wang, Chenyang Tao, Zhe Gan +7
The performance of many network learning applications crucially hinges on the success of network embedding algorithms, which aim to encode rich network information into low-dimensi…
Ouroboros: On Accelerating Training of Transformer-Based Language Models
Qian Yang, Zhouyuan Huo, Wenlin Wang +2
Language models are essential for natural language processing (NLP) tasks, such as machine translation and text summarization. Remarkable performance has been demonstrated recently…
Learning Compressed Sentence Representations for On-Device Text Processing
Dinghan Shen, Pengyu Cheng, Dhanasekar Sundararaman +5
Vector representations of sentences, trained on massive text corpora, are widely used as generic sentence embeddings across a variety of NLP problems. The learned representations a…
Decoupled Parallel Backpropagation with Convergence Guarantee
Zhouyuan Huo, Bin Gu, Qian Yang +1
Backpropagation algorithm is indispensable for the training of feedforward neural networks. It requires propagating error gradients sequentially from the output layer all the way b…
Neural Machine Translation with Pivot Languages
Yong Cheng, Yang Liu, Qian Yang +2
While recent neural machine translation approaches have delivered state-of-the-art performance for resource-rich language pairs, they suffer from the data scarcity problem for reso…
Semi-loss-tolerant strong quantum coin-flipping protocol using quantum non-demolition measurement
Qian Yang, Jia-Jun Ma, Fen-Zhuo Guo +1
In this paper, we present a semi-loss-tolerant strong quantum coin-flipping (QCF) protocol with the best bias of 0.3536. Our manuscript applies Quantum non-demolition (QND) measure…
Semi-Loss-Tolerant Strong Coin Flipping Protocol Using EPR Pairs
Jia-Jun Ma, Fen-Zhuo Guo, Qian Yang +2
In this paper, we present a quantum strong coin flipping protocol. In this protocol, an EPR pair and a quantum memory storage are made use of, and losses in the quantum communicati…