Federated Learning Framework with Lattice Joint Source-Channel Coding

Kernkonzepte

The author introduces a novel federated learning framework using lattice joint source-channel coding to enhance over-the-air computation, focusing on quantizing model parameters and leveraging interference from devices.

Zusammenfassung

The content discusses a universal federated learning framework that utilizes lattice codes for over-the-air computation. It introduces a new joint source-channel coding scheme that employs lattice codes to quantize model parameters without relying on channel state information at devices. The proposed two-layer receiver structure at the server is designed to decode an integer combination of quantized model parameters reliably for aggregation purposes. The paper highlights the effectiveness of the scheme through numerical experiments, showcasing its superiority over other over-the-air federated learning strategies.
The work addresses challenges faced by federated learning in wireless settings with network constraints, emphasizing communication issues and privacy enhancement. By incorporating lattice codes and digital communications, the proposed scheme ensures resilience against interference and noise while achieving desired learning outcomes. The experimental results demonstrate superior performance compared to existing alternatives, even under challenging channel conditions and device heterogeneity.
Key points include the development of a compute-update scheme named FedCPU, which involves end-to-end real-valued model parameter transmission using lattice codes for quantization. The transmission scheme includes normalization and dithering processes, while the aggregation scheme features adjustable weights through integer coefficients based on lattice structures. The content also discusses system insights derived from experimental results, highlighting the efficacy of FedCPU in addressing challenges posed by limited antennas at the server.

Statistiken

"K = 30"
"SNR = 10"
"M = 30"

Zitate

"The proposed scheme offers adjustable quantization, enabling distributed learning through digital modulation."
"Experimental findings showcased the superior learning accuracy of the proposed scheme."
"The content also discusses system insights derived from experimental results."

Wichtige Erkenntnisse aus

Federated Learning via Lattice Joint Source-Channel Coding

by Seyed Mohamm... um arxiv.org 03-05-2024

https://arxiv.org/pdf/2403.01023.pdf

Tiefere Fragen

How does FedCPU compare to traditional scalar quantization methods

FedCPU differs from traditional scalar quantization methods by introducing adjustable aggregation weights through integer coefficients, utilizing lattice structures and additive properties of wireless multiple-access channels. Unlike traditional methods that rely on fixed or predefined weights based on local dataset sizes, FedCPU dynamically tailors the aggregation weights to address decoding errors unique to each integer combination in its model. This adaptive approach enhances performance in imperfect communication scenarios plagued by interference and noise, a significant departure from conventional techniques.

What are the implications of varying lattice generator matrices on performance

The implications of varying lattice generator matrices on performance are crucial in FedCPU's operation. As the lattice points become more densely packed with reduced ρ values, the Voronoi regions shrink, leading to smaller quantization errors but potentially higher decoding errors due to interference and noise. Finding a balance between minimizing quantization error while managing decoding error is essential for optimal performance. Different ρ values impact the trade-off between these two types of errors, highlighting the need for careful selection based on specific system requirements.

How does FedCPU's blind approach impact its resilience against interference and noise

FedCPU's blind approach significantly enhances its resilience against interference and noise by operating without prior knowledge or channel state information at devices (CSIT). By using constant power transmission without relying on CSIT or power control mechanisms that impose constraints like average and maximum power limits, FedCPU ensures all devices can participate in learning processes regardless of their channel conditions. The scheme's reliance on an adjustable aggregation method tailored to individual communication conditions allows it to effectively combat challenges posed by interference and noise even with limited antennas at the server. This blind strategy sets FedCPU apart from existing schemes that require perfect synchronization among transmitters or extensive channel estimation training before transmission, reducing delays and improving spectral efficiency.