WE1.R2.1

Federated Learning via Lattice Joint Source-Channel Coding

Seyed Mohammad Azimi-Abarghouyi, KTH Royal Institute of Technology, Sweden; Lav R. Varshney, University of Illinois Urbana-Champaign, United States
Session:
Federated Learning
Track:
15: Distributed and Federated Learning
Location:
Ypsilon I-II-III
Presentation Time:
Wed, 10 Jul, 09:50 - 10:10
Session Chair:
Lav Varshney, University of Illinois Urbana-Champaign
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Session WE1.R2
WE1.R2.1: Federated Learning via Lattice Joint Source-Channel Coding
Seyed Mohammad Azimi-Abarghouyi, KTH Royal Institute of Technology, Sweden; Lav R. Varshney, University of Illinois Urbana-Champaign, United States
WE1.R2.2: Hierarchical Federated Learning: The Interplay of User Mobility and Data Heterogeneity
Wei Dong, Zhejiang University, China; Chenyuan Feng, EURECOM, France, France; Chen Sun, Sony (China) Research Laboratory, China; Howard H. Yang, Zhejiang University, China
WE1.R2.3: Computation Selection: Scheduling Users to Enable Over-the-Air Federated Learning
Bobak Nazer, Boston University, United States; Krishna Narayanan, Texas A&M University, United States
WE1.R2.4: Causal Impact Analysis for Asynchronous Decision Making
Mert Kayaalp, Yunus Inan, École Polytechnique Fédérale de Lausanne, Switzerland; Visa Koivunen, Aalto University, Finland; Ali H. Sayed, École Polytechnique Fédérale de Lausanne, Switzerland
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