Sponsors

Goals

The past decade has witnessed wide applications of machine learning to various domains for decision-making, including crime detection, urban planning, drug discovery, and health monitoring, which benefited from surging data resources. As data collection in real-world applications is often done in different locations, being able to mine and discover knowledge from distributed data sources is an essential requirement for building powerful predictive models. However, directly uploading all data sources to an untrustworthy centralized data server for learning will lead to great risks of privacy leakage. Federated Learning (FL) emerges as a decentralized learning framework that aggregates knowledge from distributed data without centralizing them, hence encouraging privacy-preserving machine learning. By hosting this workshop at SIGKDD, we aim to attract a broad spectrum of audiences, including researchers and practitioners from academia and industry interested in the latest advances in FL. As an effort to advance the fundamental development of FL in data mining, this workshop will encourage ideas exchange on the trustworthiness, scalability, robustness, and a wide range of applications of FL.

Invited Speakers

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Jian Pei

Duke University

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Salman Avestimehr

USC & FedML

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Yiran Chen

Duke University

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Carl Yang

Emory University

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Heiko Ludwig

IBM Research

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Tian Li

Carnegie Mellon University

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Aidong Zhang

University of Virginia

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Ananda T. Suresh

Google Research

Organizers

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Junyuan Hong

[Primary Contact]
Michigan State University

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Zhuangdi Zhu

Microsoft

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Lingjuan Lyu

Sony AI

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Vishnu Naresh Boddeti

Michigan State University

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Yang Zhou

Auburn University

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Jiayu Zhou

Michigan State University

Volunteers

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Siqi Liang

Michigan State University

Program Committee Members

(sorted alphabetically)

  • Anirban Das (Capital One)
  • Chaochao Chen (Zhejiang University)
  • Chulin Xie (UIUC)
  • Enmao Diao (Duke University)
  • Fan Liu (Hong Kong University of Science and Technology (Guangzhou))
  • Guangjing Wang (Michigan State University)
  • Han Xie (Emory University)
  • Jiahua Dong (Chinese Academy of Sciences)
  • Jian Xu (Tsinghua University)
  • Jianing Zhu (Hong Kong Baptist University)
  • Jiaqi Wang (Pennsylvania State University)
  • Jingtao Li (Arizona State University)
  • Jun Zhang (The Hong Kong University of Science and Technology)
  • Kevin Hsieh (Microsoft)
  • Kumar Kshitij Patel (Toyota Technological Institute at Chicago)
  • Ruixuan Liu (Renmin University of China)
  • Shuyang Yu (Michigan State University)
  • Siqi Liang (Michigan State University)
  • Songze Li (The Hong Kong University of Science and Technology)
  • Tao Lin (Westlake University)
  • Weiming Zhuang (Sony AI)
  • Xuefeng Jiang (Chinese Academy of Sciences)
  • Yangsibo Huang (Princeton University)
  • Yue Tan (University of Technology Sydney)
  • Yuhang Yao (Carnegie Mellon University)
  • Yuyang Deng (Pennsylvania State University)
  • Zeyu Qin (The Hong Kong University of Science and Technology)
  • Zuobin Xiong (Georgia State University)