Technical Program

TP7a: Machine Learning and Optimization in Distributed Networks (Invited)

Session Type: Oral
Time: Tuesday, November 5, 13:30 - 15:10
Location: Acacia
Session Chair: Virginia Smith, Carnegie Mellon University
 
TP7a-1: RING-GD: RUN RING-ALLREDUCE SIMULTANEOUSLY WITH GRADIENT DESCENT
         Kun Yuan; Alibaba Group
         Xianghui Mao; Tsinghua University
         Wotao Yin; Alibaba Group
 
TP7a-2: UNDERSTANDING THE LIMITS OF COMMUNICATION EFFICIENT DISTRIBUTED TRAINING
         Hongyi Wang; University of Wisconsin-Madison
         Saurabh Agarwal; University of Wisconsin-Madison
         Zachary Charles; University of Wisconsin-Madison
         Shivaram Venkataraman; University of Wisconsin-Madison
         Dimitris Papailiopoulos; University of Wisconsin-Madison
 
TP7a-3: FEDERATED LEARNING WITH AUTOTUNED COMMUNICATION-EFFICIENT SECURE AGGREGATION
         Keith Bonawitz; Google
         Fariborz Salehi; California Institute of Technology
         Jakub Konečný; Google
         Brendan McMahan; Google
         Marco Gruteser; Google
 
TP7a-4: FEDDANE: A FEDERATED NEWTON-TYPE METHOD
         Tian Li; Carnegie Mellon University
         Anit Kumar Sahu; Bosch Center for Artificial Intelligence
         Manzil Zaheer; Google Research
         Maziar Sanjabi; University of Southern California
         Ameet Talwalkar; Carnegie Mellon University, Determined AI
         Virginia Smith; Carnegie Mellon University