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Knowledge Distillation for Collaborative Learning in Distributed Communications and Sensing

Nhan Thanh Nguyen, Mengyuan Ma, Nir Shlezinger, Junil Choi, Yonina C. Eldar, A. Lee Swindlehurst, Markku Juntti

Abstract

The rise of sixth generation (6G) wireless networks promises to deliver ultra-reliable, low-latency, and energy-efficient communications, sensing, and computing. However, traditional centralized artificial intelligence (AI) paradigms are ill-suited to the decentralized, resource-constrained, and dynamic nature of 6G ecosystems. This paper explores knowledge distillation (KD) and collaborative learning as promising techniques that enable the efficient and scalable deployment of lightweight AI models across distributed communications and sensing (C&S) nodes. We begin by providing an overview of KD and highlight the key strengths that make it particularly effective in distributed scenarios characterized by device heterogeneity, task diversity, and constrained resources. We then examine its role in fostering collective intelligence through collaborative learning between the central and distributed nodes via various knowledge distilling and deployment strategies. Finally, we present a systematic numerical study demonstrating that KD-empowered collaborative learning can effectively support lightweight AI models for multi-modal sensing-assisted beam tracking applications with substantial performance gains and complexity reduction.

Knowledge Distillation for Collaborative Learning in Distributed Communications and Sensing

Abstract

The rise of sixth generation (6G) wireless networks promises to deliver ultra-reliable, low-latency, and energy-efficient communications, sensing, and computing. However, traditional centralized artificial intelligence (AI) paradigms are ill-suited to the decentralized, resource-constrained, and dynamic nature of 6G ecosystems. This paper explores knowledge distillation (KD) and collaborative learning as promising techniques that enable the efficient and scalable deployment of lightweight AI models across distributed communications and sensing (C&S) nodes. We begin by providing an overview of KD and highlight the key strengths that make it particularly effective in distributed scenarios characterized by device heterogeneity, task diversity, and constrained resources. We then examine its role in fostering collective intelligence through collaborative learning between the central and distributed nodes via various knowledge distilling and deployment strategies. Finally, we present a systematic numerical study demonstrating that KD-empowered collaborative learning can effectively support lightweight AI models for multi-modal sensing-assisted beam tracking applications with substantial performance gains and complexity reduction.
Paper Structure (17 sections, 4 figures, 2 tables)

This paper contains 17 sections, 4 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Knowledge Distillation for Distributed C&S
  3. KD Process
  4. Types of Knowledge
  5. Distillation Schemes
  6. KD for Distributed C&S
  7. Integrating KD in Distributed C&S Networks
  8. Motivation and Rationale
  9. KD Strategies for Distributed C&S via Collaborative Learning
  10. Model Deployments and Coordination
  11. Numerical Example
  12. Challenges and Open Research Directions
  13. Optimized Loss Functions
  14. Enhancing Student Model's Autonomy
  15. Privacy and Security Challenges
  16. ...and 2 more sections

Figures (4)

  • Figure 1: Collaborative learning in distributed C&S networks.
  • Figure 2: Different types of knowledge and distillation schemes in KD gou2021knowledge.
  • Figure 3: Three knowledge distilling topologies with dynamic deployments of teacher and student models in collaborative learning for distributed C&S networks.
  • Figure 4: Beam prediction performance for teacher and student models across current and future time slots, with and without self-KD, response-based KD, and relation-based KD, using mono-modal (image only) or multi-modal (image & radar) sensing.