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
Abstract
This paper introduces a universal federated learning framework that enables over-the-air computation via digital communications, using a new joint source-channel coding scheme. Without relying on channel state information at devices, this scheme employs lattice codes to both quantize model parameters and exploit interference from the devices. A novel two-layer receiver structure at the server is designed to reliably decode an integer combination of the quantized model parameters as a lattice point for the purpose of aggregation. Numerical experiments validate the effectiveness of the proposed scheme. Even with the challenges posed by channel conditions and device heterogeneity, the proposed scheme markedly surpasses other over-the-air FL strategies.
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