211 citations · 940 across the 32 of their papers we have counts for
7 papers · 1 filter
LINDT: Tackling Negative Federated Learning with Local Adaptation
Hong Lin, Lidan Shou, Ke Chen +2
Federated Learning (FL) is a promising distributed learning paradigm, which allows a number of data owners (also called clients) to collaboratively learn a shared model without dis…
BERT-JAM: Boosting BERT-Enhanced Neural Machine Translation with Joint Attention
Zhebin Zhang, Sai Wu, Dawei Jiang +1
BERT-enhanced neural machine translation (NMT) aims at leveraging BERT-encoded representations for translation tasks. A recently proposed approach uses attention mechanisms to fuse…
MLCask: Efficient Management of Component Evolution in Collaborative Data Analytics Pipelines
Zhaojing Luo, Sai Ho Yeung, Meihui Zhang +7
With the ever-increasing adoption of machine learning for data analytics, maintaining a machine learning pipeline is becoming more complex as both the datasets and trained models e…
Privacy Preserving Vertical Federated Learning for Tree-based Models
Yuncheng Wu, Shaofeng Cai, Xiaokui Xiao +2
Federated learning (FL) is an emerging paradigm that enables multiple organizations to jointly train a model without revealing their private data to each other. This paper studies…
ForkBase: Immutable, Tamper-evident Storage Substrate for Branchable Applications
Qian Lin, Kaiyuan Yang, Tien Tuan Anh Dinh +8
Data collaboration activities typically require systematic or protocol-based coordination to be scalable. Git, an effective enabler for collaborative coding, has been attested for…
A Transactional Perspective on Execute-order-validate Blockchains
Pingcheng Ruan, Dumitrel Loghin, Quang-Trung Ta +3
Smart contracts have enabled blockchain systems to evolve from simple cryptocurrency platforms, such as Bitcoin, to general transactional systems, such as Ethereum. Catering for em…