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

Yipeng Liang, Qimei Chen, Hao Jiang

TL;DR

A general FL-ISCC framework is investigated, implementing both FedAVG-ISCC and FedSGD-ISCC and providing a theoretical analysis and comparison, revealing that both sample collection and communication errors negatively impact algorithm performance, highlighting the need for careful design to optimize FL-ISCC applications.

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.

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

TL;DR

A general FL-ISCC framework is investigated, implementing both FedAVG-ISCC and FedSGD-ISCC and providing a theoretical analysis and comparison, revealing that both sample collection and communication errors negatively impact algorithm performance, highlighting the need for careful design to optimize FL-ISCC applications.

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.
Paper Structure (26 sections, 7 theorems, 56 equations, 5 figures)

This paper contains 26 sections, 7 theorems, 56 equations, 5 figures.

Table of Contents

  1. Introduction
  2. Related work
  3. Motivation and contribution
  4. System model
  5. FL-ISCC framework
  6. Global model update for FedAVG-ISCC
  7. Global model update for FedSGD-ISCC
  8. Communication Model
  9. Computation Model
  10. Convergence Analysis and Performance Evaluation
  11. Assumptions and preliminaries
  12. Convergence analysis for FedAVG-ISCC
  13. Convergence Analysis for FedSGD-ISCC
  14. Performance analysis and discussion
  15. Performance analysis to IID setting
  16. ...and 11 more sections

Key Result

Lemma 1

Given the datasets $\mathcal{S}^{n}_{t-1}$ and $\mathcal{D}^{n}_{t}$ in the $t$-th communication round, the aggregated gradient $\sum_{n=1}^{N} \rho^{n} \nabla F(\mathbf{w}^{n}_{t-1}; \mathcal{S}^{n}_{t})$ satisfies the following equation. where $D_t = \sum^{N}_{n=1} D^{n}_{t}$, $\Bar{\rho}^{n} = \frac{{S}^{n}_{t-1}}{{S}^{}_{t-1}}$ and $\Tilde{\rho}^{n} = \frac{{D}^{n}_{t}}{{D}^{}_{t}}$.

Figures (5)

  • Figure 1: Illustration of the proposed SC$^2$-FEEL.
  • Figure 2: Performance comparison between FedSGD-ISCC and FedAVG-ISCC under IID settings.
  • Figure 3: Performance comparison between FedAVG-ISCC and FedSGD-ISCC under different communication errors.
  • Figure 4: Performance comparison between FedAVG-ISCC and FedSGD-ISCC under different Non-IID settings.
  • Figure 5: The effectiveness of our proposed FL-ISCC framework.

Theorems & Definitions (10)

  • Lemma 1
  • proof : proof
  • Lemma 2
  • proof : proof
  • Theorem 1
  • Lemma 3
  • proof : proof
  • Theorem 2
  • Corollary 1
  • Corollary 2