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Efficient Integration of Distributed Learning Services in Next-Generation Wireless Networks

Paul Zheng, Navid Keshtiarast, Pradyumna Kumar Bishoyi, Yao Zhu, Yulin Hu, Marina Petrova, Anke Schmeink

TL;DR

The paper tackles the challenge of integrating distributed learning into 6G networks while coexisting with HB traffic by introducing a time-dependent, session-based resource allocation framework that operates within a single communication round. It jointly optimizes downlink/uplink durations, RB sharing, and computation speeds under large-scale coherence time, and addresses non-convexities with a quadratic transform and majorization-minimization, solved via an iterative convex optimization procedure. The approach yields substantial improvements in latency and energy consumption compared to rigid allocation and HB-as-FL baselines, validated through simulations with heterogeneous DL and HB traffic. The framework demonstrates the importance of exploiting time dynamics and heterogeneity for accurate latency/energy estimation and efficient DL deployment in future wireless networks.

Abstract

Distributed learning (DL) is considered a cornerstone of intelligence enabler, since it allows for collaborative training without the necessity for local clients to share raw data with other parties, thereby preserving privacy and security. Integrating DL into the 6G networks requires coexistence design with existing services such as high-bandwidth (HB) traffic like eMBB. Current designs in the literature mainly focus on communication round (CR)-wise designs that assume a fixed resource allocation during each CR. However, fixed resource allocation within a CR is a highly inefficient and inaccurate representation of the system's realistic behavior. This is due to the heterogeneous nature of the system, where clients inherently need to access the network at different times. This work zooms into one arbitrary communication round and demonstrates the importance of considering a time-dependent resource-sharing design with HB traffic. We propose a time-dependent optimization problem for minimizing the consumed time and energy by DL within the CR. Due to its intractability, a session-based optimization problem has been proposed assuming a large-scale coherence time. An iterative algorithm has been designed to solve such problems and simulation results confirm the importance of such efficient and accurate integration design.

Efficient Integration of Distributed Learning Services in Next-Generation Wireless Networks

TL;DR

The paper tackles the challenge of integrating distributed learning into 6G networks while coexisting with HB traffic by introducing a time-dependent, session-based resource allocation framework that operates within a single communication round. It jointly optimizes downlink/uplink durations, RB sharing, and computation speeds under large-scale coherence time, and addresses non-convexities with a quadratic transform and majorization-minimization, solved via an iterative convex optimization procedure. The approach yields substantial improvements in latency and energy consumption compared to rigid allocation and HB-as-FL baselines, validated through simulations with heterogeneous DL and HB traffic. The framework demonstrates the importance of exploiting time dynamics and heterogeneity for accurate latency/energy estimation and efficient DL deployment in future wireless networks.

Abstract

Distributed learning (DL) is considered a cornerstone of intelligence enabler, since it allows for collaborative training without the necessity for local clients to share raw data with other parties, thereby preserving privacy and security. Integrating DL into the 6G networks requires coexistence design with existing services such as high-bandwidth (HB) traffic like eMBB. Current designs in the literature mainly focus on communication round (CR)-wise designs that assume a fixed resource allocation during each CR. However, fixed resource allocation within a CR is a highly inefficient and inaccurate representation of the system's realistic behavior. This is due to the heterogeneous nature of the system, where clients inherently need to access the network at different times. This work zooms into one arbitrary communication round and demonstrates the importance of considering a time-dependent resource-sharing design with HB traffic. We propose a time-dependent optimization problem for minimizing the consumed time and energy by DL within the CR. Due to its intractability, a session-based optimization problem has been proposed assuming a large-scale coherence time. An iterative algorithm has been designed to solve such problems and simulation results confirm the importance of such efficient and accurate integration design.

Paper Structure

This paper contains 23 sections, 2 theorems, 28 equations, 5 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model and Initial Problem Formulation
  3. High Rate UEs (e.g., eMBB)
  4. Vanilla Federated Learning
  5. General Problem Formulation
  6. Rigid Resource Allocation (as baseline) and "Static" Channel Assumption
  7. Session-Aware RB allocation
  8. Motivation
  9. Session-aware RB allocation
  10. Session-based Problem Formulation
  11. Session-based Problem Formulation
  12. Non-Convexity Handling
  13. Maximizing Product
  14. Minimizing Product Term
  15. Algorithm
  16. ...and 8 more sections

Key Result

Theorem 1

Given fixed auxiliary variables $Y\in\mathcal{Y}$, the subproblem $(\mathcal{T}\mathcal{P})$ is a convex optimization problem.

Figures (5)

  • Figure 1: Example Illustration of the System Time-wise RB allocation for homogeneous and heterogeneous system
  • Figure 2: Algorithm convergence
  • Figure 3: Rigid ranking evaluation with $S=7$
  • Figure 4: Energy-time Pareto front
  • Figure 5: Parameter influence when fixed energy constraint is imposed.

Theorems & Definitions (3)

  • Definition 1: Session
  • Theorem 1
  • Theorem 2: Shen_FP_quadraticTransform_2018