2020 IEEE International Symposium
on Information Theory
21-26 June 2020 • Los Angeles, California, USA
L.5: Federated Learning | |
| Session Type: Lecture | |
| Track: Statistics and Learning Theory | |
| Virtual Session: View on Virtual Platform | |
| Session Chair: Changho Suh, Korea Advanced Institute of Science and Technology | |
| L.5.1: Federated Recommendation System via Differential Privacy | |
| Click here to download the manuscript | |
| Click here to view the Virtual Presentation | |
| Tan Li; City University of Hong Kong | |
| Linqi Song; City University of Hong Kong | |
| Christina Fragouli; University of California, Los Angeles | |
| L.5.2: Update Aware Device Scheduling for Federated Learning at the Wireless Edge | |
| Click here to download the manuscript | |
| Click here to view the Virtual Presentation | |
| Mohammad Mohammadi Amiri; Princeton University | |
| Deniz Gunduz; Imperial College London | |
| Sanjeev R. Kulkarni; Princeton University | |
| H. Vincent Poor; Princeton University | |
| L.5.3: Wireless Federated Learning with Local Differential Privacy | |
| Click here to download the manuscript | |
| Click here to view the Virtual Presentation | |
| Mohamed Seif; University of Arizona | |
| Ravi Tandon; University of Arizona | |
| Ming Li; University of Arizona | |
| L.5.4: The Communication-Aware Clustered Federated Learning Problem | |
| Click here to download the manuscript | |
| Click here to view the Virtual Presentation | |
| Nir Shlezinger; Weizmann Institute of Science | |
| Stefano Rini; National Chiao Tung University | |
| Yonina Eldar; Weizmann Institute of Science | |
Plan Ahead
2021 IEEE International Symposium on Information Theory
11-16 July 2021 | Melbourne, Victoria, Australia
