מושגי ליבה
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.
תקציר
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.
סטטיסטיקה
"K = 30"
"SNR = 10"
"M = 30"
ציטוטים
"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."