報告題目：Variance Reduced Median-of-Means Estimator for Byzantine-Robust Distributed Inference
報 告 人：劉衛東教授 上海交通大學
報告時間：2020年7月2日 13:30-14:30
報告地點：騰訊會議 ID：258 627 982
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https://meeting.tencent.com/s/EAxv6oR5Ry39
校内聯系人：朱複康 fzhu@jlu.edu.cn
報告摘要：
This paper develops an efficient distributed inference algorithm, which is robust against a moderate fraction of Byzantine nodes, namely arbitrary and possibly adversarial machines in a distributed learning system. In robust statistics, the median-of-means (MOM) has been a popular approach to hedge against Byzantine failures due to its ease of implementation and computational efficiency. However, the MOM estimator has the shortcoming in terms of statistical efficiency. The first main contribution of the paper is to propose a variance reduced median-of-means (VRMOM) estimator, which improves the statistical efficiency over the vanilla MOM estimator and is computationally as efficient as the MOM. Based on the proposed VRMOM estimator, we develop a general distributed inference algorithm that is robust against Byzantine failures. Theoretically, our distributed algorithm achieves a fast convergence rate with only a constant number of rounds of communications. We also provide the asymptotic normality result for the purpose of statistical inference. To the best of our knowledge, this is the first normality result in the setting of Byzantine-robust distributed learning. The simulation results are also presented to illustrate the effectiveness of our method.
報告人簡介：
劉衛東，上海交通大學數學科學學院副院長，特聘教授，國家傑出青年科學基金獲得者。2008年于浙江大學獲博士學位，2008-2011年在香港科技大學、美國賓夕法尼亞大學沃頓商學院從事博士後研究工作。2010年獲全國百篇優秀博士學位論文獎及由世界華人數學家大會頒發的新世界數學獎；2013年獲得國家優秀青年科學基金；2016年獲得國家“萬人計劃”青年拔尖人才；2018年獲國家傑出青年科學基金。研究興趣包括現代統計學、機器學習等，在統計學四大頂級期刊（AOS，JASA，JRSSB，Biometrika）和機器學習頂級期刊JMLR發表40餘篇論文。
