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Technical Talks Session 2
Jinhyun So · Chong Liu · Honglin Yuan · Krishna Pillutla · Leighton P Barnes · Ashkan Yousefpour · Swanand Kadhe

Sat Jul 18 10:45 AM -- 12:10 PM (PDT) @
  1. Jinhyun So, Basak Guler and A. Salman Avestimehr. Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning
  2. Chong Liu, Yuqing Zhu, Kamalika Chaudhuri and Yu-Xiang Wang. Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning
  3. Honglin Yuan and Tengyu Ma. Federated Accelerated Stochastic Gradient Descent
  4. Krishna Pillutla, Sham Kakade and Zaid Harchaoui. Robust Aggregation for Federated Learning
  5. Leighton Pate Barnes, Huseyin A. Inan, Berivan Isik and Ayfer Ozgur. rTop-k: A Statistical Estimation Approach to Distributed SGD
  6. Ashkan Yousefpour, Brian Nguyen, Siddartha Devic, Guanhua Wang, Abdul Rahman Kreidieh, Hans Lobel, Alexandre Bayen and Jason Jue. ResiliNet: Failure-Resilient Inference in Distributed Neural Networks
  7. Swanand Kadhe, Nived Rajaraman, O. Ozan Koyluoglu and Kannan Ramchandran. FastSecAgg: Scalable Secure Aggregation for Privacy-Preserving Federated Learning

Author Information

Jinhyun So (University of Southern California)
Chong Liu (University of California, Santa Barbara)
Honglin Yuan (Stanford University)
Krishna Pillutla (University of Washington)

PhD student at the University of Washington. Advisors: Zaid Harchaoui and Sham Kakade Interests: ML/Optimization, structured prediction, federated learning

Leighton P Barnes (Stanford University)
Ashkan Yousefpour (Facebook AI)

Ashkan is currently a Research Scientist at Facebook AI. He works on privacy-preserving machine learning and federated learning, and contributes to PyTorch. He was a Visiting Researcher at University of California, Berkeley and member of the Berkeley Artificial Intelligence Research (BAIR). When he was at UC Berkeley, he contributed to FLOW, a framework for deep reinforcement learning in traffic control. Ashkan finished his PhD in Computer Science at the University of Texas at Dallas. His research was on intersection of Systems, Edge Computing, and Machine Learning, and he worked on improving quality of service in IoT and Deep Leanring Applications through Fog Computing. We he was at UT Dallas, he created Fog Computing Conference Hub.

Swanand Kadhe (University of California Berkeley)

Swanand Kadhe is a postdoctoral researcher in the EECS Department at UC Berkeley. He earned his Ph.D. degree in Electrical Engineering from Texas A&M University, College Station, in 2017. He is a recipient of the 2016 Graduate Teaching Fellowship from TAMU College of Engineering. He has been a visiting researcher at Nokia Bell Labs, Duke University, and The Chinese University of Hong Kong. From 2009 to 2012, he was a research engineer at the Innovation Labs of TATA Consultancy Services, Bangalore. His research interests are in Distributed Machine Learning, Privacy and Security, Blockchains, Information Theory, Coding Theory, and Signal Processing.

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