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Latency-aware Multimodal Federated Learning over UAV Networks

Shaba Shaon, Dinh C. Nguyen

TL;DR

The paper tackles latency in UAV-enabled Federated Multimodal Learning by formulating a joint optimization of sensing scheduling, UAV trajectory, and cross-layer resource allocation, under a non-convex loss with convergence guarantees. It adopts an attention-based fusion of multimodal embeddings at a UAV-BS architecture and proves a convergence bound that scales with rounds, UAVs, and modalities. To solve the latency minimization, the authors develop a BCD-SCA algorithm that decomposes the problem into three convexified sub-problems, enabling efficient iterative optimization. Empirical results show substantial latency reductions (up to 42.49%) and improved IID/non-IID learning performance, demonstrating the practical impact of latency-aware FML in dynamic UAV networks.

Abstract

This paper investigates federated multimodal learning (FML) assisted by unmanned aerial vehicles (UAVs) with a focus on minimizing system latency and providing convergence analysis. In this framework, UAVs are distributed throughout the network to collect data, participate in model training, and collaborate with a base station (BS) to build a global model. By utilizing multimodal sensing, the UAVs overcome the limitations of unimodal systems, enhancing model accuracy, generalization, and offering a more comprehensive understanding of the environment. The primary objective is to optimize FML system latency in UAV networks by jointly addressing UAV sensing scheduling, power control, trajectory planning, resource allocation, and BS resource management. To address the computational complexity of our latency minimization problem, we propose an efficient iterative optimization algorithm combining block coordinate descent and successive convex approximation techniques, which provides high-quality approximate solutions. We also present a theoretical convergence analysis for the UAV-assisted FML framework under a non-convex loss function. Numerical experiments demonstrate that our FML framework outperforms existing approaches in terms of system latency and model training performance under different data settings.

Latency-aware Multimodal Federated Learning over UAV Networks

TL;DR

The paper tackles latency in UAV-enabled Federated Multimodal Learning by formulating a joint optimization of sensing scheduling, UAV trajectory, and cross-layer resource allocation, under a non-convex loss with convergence guarantees. It adopts an attention-based fusion of multimodal embeddings at a UAV-BS architecture and proves a convergence bound that scales with rounds, UAVs, and modalities. To solve the latency minimization, the authors develop a BCD-SCA algorithm that decomposes the problem into three convexified sub-problems, enabling efficient iterative optimization. Empirical results show substantial latency reductions (up to 42.49%) and improved IID/non-IID learning performance, demonstrating the practical impact of latency-aware FML in dynamic UAV networks.

Abstract

This paper investigates federated multimodal learning (FML) assisted by unmanned aerial vehicles (UAVs) with a focus on minimizing system latency and providing convergence analysis. In this framework, UAVs are distributed throughout the network to collect data, participate in model training, and collaborate with a base station (BS) to build a global model. By utilizing multimodal sensing, the UAVs overcome the limitations of unimodal systems, enhancing model accuracy, generalization, and offering a more comprehensive understanding of the environment. The primary objective is to optimize FML system latency in UAV networks by jointly addressing UAV sensing scheduling, power control, trajectory planning, resource allocation, and BS resource management. To address the computational complexity of our latency minimization problem, we propose an efficient iterative optimization algorithm combining block coordinate descent and successive convex approximation techniques, which provides high-quality approximate solutions. We also present a theoretical convergence analysis for the UAV-assisted FML framework under a non-convex loss function. Numerical experiments demonstrate that our FML framework outperforms existing approaches in terms of system latency and model training performance under different data settings.

Paper Structure

This paper contains 22 sections, 3 theorems, 82 equations, 11 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Works
  3. Motivations and Key Contributions
  4. System Model
  5. FML Model Formulation over UAV Networks
  6. Convergence Analysis for Proposed Multimodal FL Framework
  7. Notation and Definition
  8. Assumptions
  9. Convergence Rates
  10. Detailed Proofs for Convergence Analysis
  11. Proof of Lemma \ref{['lemma1']}
  12. Proof of Lemma \ref{['lemma2']}
  13. Proof of Lemma \ref{['lemma3']}
  14. Proof of Theorem 1
  15. Latency Optimization for the Proposed Multimodal FL Framework
  16. ...and 7 more sections

Key Result

Lemma 2.1

Let Assumption Assumption1 hold, the expected value of the inner product between the stochastic gradient and full gradient is limited by

Figures (11)

  • Figure 1: Proposed FML framework over UAV networks with five key steps: (1) UAVs sense data from the ground object, (2) train local models on the sensed data, (3) upload embeddings and models to the Base Station (BS), (4) the BS trains a decoder model using concatenated embeddings, and (5) the BS aggregates local models to create a unified global model, sending it back to the UAVs.
  • Figure 2: The encoder-decoder architecture in the proposed FML framework.
  • Figure 3: Comparison of various FML approaches on UCI HAR dataset.
  • Figure 4: Performance of our proposed FML scheme as the number of UAVs increases.
  • Figure 5: Latency comparison.
  • ...and 6 more figures

Theorems & Definitions (8)

  • Lemma 2.1
  • proof
  • Lemma 2.2
  • proof
  • Lemma 2.3
  • proof
  • proof
  • Remark 2.1