SS-6.1
Federated Learning Challenges and Opportunities: An Outlook
Jie Ding, Eric Tramel, Anit Kumar Sahu, Shuang Wu, Salman Avestimehr, Tao Zhang, Amazon, United States of America
Session:
Frontiers of Federated Learning: Applications, Challenges, and Opportunities
Track:
Special Sessions
Location:
Gather Area A
Presentation Time:
Mon, 9 May, 22:00 - 22:45 China Time (UTC +8)
Mon, 9 May, 14:00 - 14:45 UTC
Mon, 9 May, 14:00 - 14:45 UTC
Session Co-Chairs:
Jie Ding, University of Minnesota and Alexa AI, Amazon and Salman Avestimehr, University of Southern California and Alexa AI, Amazon and Tao Zhang, Alexa AI, Amazon
Session SS-6
SS-6.1: Federated Learning Challenges and Opportunities: An Outlook
Jie Ding, Eric Tramel, Anit Kumar Sahu, Shuang Wu, Salman Avestimehr, Tao Zhang, Amazon, United States of America
SS-6.2: ENABLING ON-DEVICE TRAINING OF SPEECH RECOGNITION MODELS WITH FEDERATED DROPOUT
Dhruv Guliani, Lillian Zhou, Changwan Ryu, Tien-Ju Yang, Harry Zhang, Yonghui Xiao, Francoise Beaufays, Giovanni Motta, Google Inc, United States of America
SS-6.3: ADAPTIVE NODE PARTICIPATION FOR STRAGGLER-RESILIENT FEDERATED LEARNING
Amirhossein Reisizadeh, MIT, United States of America; Isidoros Tziotis, Aryan Mokhtari, UT Austin, United States of America; Hamed Hassani, UPenn, United States of America; Ramtin Pedarsani, UC Santa Barbara, United States of America
SS-6.4: LEARNINGS FROM FEDERATED LEARNING IN THE REAL WORLD
Christophe Dupuy, Tanya Roosta, Leo Long, Clement Chung, Rahul Gupta, Amazon Alexa AI, United States of America; Salman Avestimehr, Amazon Alexa AI ; University of Southern California (USC), United States of America
SS-6.5: A DYNAMIC REWEIGHTING STRATEGY FOR FAIR FEDERATED LEARNING
Zhiyuan Zhao, Gauri Joshi, Carnegie Mellon University, United States of America
SS-6.6: OVER-THE-AIR PERSONALIZED FEDERATED LEARNING
Hasin Us Sami, Basak Guler, University of California, Riverside, United States of America