Federated Learning with Integrated Sensing, Communication, and Computation: Frameworks and Performance Analysis

TL;DR

This paper introduces a comprehensive FL-ISCC framework for 6G, analyzing its algorithms' performance and robustness, demonstrating potential for reduced latency and energy use, and providing theoretical insights into their behaviors under various data and error conditions.

Contribution

The paper presents a general FL-ISCC framework with theoretical analysis and experimental validation, comparing FedAVG-ISCC and FedSGD-ISCC under different data distributions and error scenarios.

Findings

FedAVG-ISCC outperforms FedSGD-ISCC on IID data due to multiple local updates.

FedSGD-ISCC is more robust under non-IID data and communication errors.

ISCC framework significantly reduces latency and energy consumption in federated learning.

Abstract

With the emergence of integrated sensing, communication, and computation (ISCC) in the upcoming 6G era, federated learning with ISCC (FL-ISCC), integrating sample collection, local training, and parameter exchange and aggregation, has garnered increasing interest for enhancing training efficiency. Currently, FL-ISCC primarily includes two algorithms: FedAVG-ISCC and FedSGD-ISCC. However, the theoretical understanding of the performance and advantages of these algorithms remains limited. To address this gap, we investigate a general FL-ISCC framework, implementing both FedAVG-ISCC and FedSGD-ISCC. We experimentally demonstrate the substantial potential of the ISCC framework in reducing latency and energy consumption in FL. Furthermore, we provide a theoretical analysis and comparison. The results reveal that:1) Both sample collection and communication errors negatively impact algorithm performance, highlighting the need for careful design to optimize FL-ISCC applications. 2) FedAVG-ISCC performs better than FedSGD-ISCC under IID data due to its advantage with multiple local updates. 3) FedSGD-ISCC is more robust than FedAVG-ISCC under non-IID data, where the multiple local updates in FedAVG-ISCC worsen performance as non-IID data increases. FedSGD-ISCC maintains performance levels similar to IID conditions. 4) FedSGD-ISCC is more resilient to communication errors than FedAVG-ISCC, which suffers from significant performance degradation as communication errors increase.Extensive simulations confirm the effectiveness of the FL-ISCC framework and validate our theoretical analysis.