4 papers presented at SIGMOD 2021

The following papers authored by researchers at ETH have been presented at ACM SIGMOD/PODS 2021:

Towards Demystifying Serverless Machine Learning Training
Jiawei Jiang (ETH Zurich)*; Shaoduo Gan (ETH Zurich); Yue Liu (ETH Zurich); Fanlin Wang (ETHZ); Gustavo Alonso (ETHZ); Ana Klimovic (ETH Zurich); Ankit Singla (ETH Zurich); Wentao Wu (Microsoft Research); Ce Zhang (ETH)

VF^2Boost: Very Fast Vertical Federated Gradient Boosting for Cross-Enterprise Learning
Fangcheng Fu (Peking University)*; Yingxia Shao (BUPT); Lele Yu (Peking University); Jiawei Jiang (ETH Zurich); Huanran Xue (Tencent Inc.); Yangyu Tao (Tencent); Bin Cui (Peking University)

Heterogeneity-Aware Distributed Machine Learning Training via Partial Reduce
Xupeng Miao (Peking University)*; Xiaonan Nie (Peking University); Yingxia Shao (BUPT); Zhi Yang (Peking University); Jiawei Jiang (ETH Zurich); Lingxiao Ma (Peking University); Bin Cui (Peking University)

CoRM: Compactable Remote Memory over RDMA
Konstantin Taranov (ETH Zurich)*; Salvatore Di Girolamo (ETH Zurich); Torsten Hoefler (ETH Zurich)