most citedFederated Multiple Label Hashing (FedMLH): Communication Efficient Federated Learning on Extreme Classification Tasks

2 citations · 2 across the 1 of their papers we have counts for

collaborators

2 papers

cs.LG2021★ 2 cited

Federated Multiple Label Hashing (FedMLH): Communication Efficient Federated Learning on Extreme Classification Tasks

Zhenwei Dai, Chen Dun, Yuxin Tang +2

Federated learning enables many local devices to train a deep learning model jointly without sharing the local data. Currently, most of federated training schemes learns a global m…

cs.NI2019

Programmable In-Network Security for Context-aware BYOD Policies

Qiao Kang, Lei Xue, Adam Morrison +3

Bring Your Own Device (BYOD) has become the new norm in enterprise networks, but BYOD security remains a top concern. Context-aware security, which enforces access control based on…