all AI news
Federated Learning via Lattice Joint Source-Channel Coding
March 5, 2024, 2:43 p.m. | Seyed Mohammad Azimi-Abarghouyi, Lav R. Varshney
cs.LG updates on arXiv.org arxiv.org
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 …
abstract arxiv coding communications computation cs.it cs.lg devices digital exploit federated learning framework information interference lattice math.it novel paper parameters state type universal via
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
Artificial Intelligence – Bioinformatic Expert
@ University of Texas Medical Branch | Galveston, TX
Lead Developer (AI)
@ Cere Network | San Francisco, US
Research Engineer
@ Allora Labs | Remote
Ecosystem Manager
@ Allora Labs | Remote
Founding AI Engineer, Agents
@ Occam AI | New York
AI Engineer Intern, Agents
@ Occam AI | US