Knowledge Distillation for Collaborative Learning in Distributed Communications and Sensing

TL;DR

This paper investigates how knowledge distillation can enable efficient, scalable collaborative AI in 6G wireless networks, addressing decentralization and resource constraints for sensing and communication tasks.

Contribution

It provides an overview of knowledge distillation techniques and demonstrates their effectiveness in distributed 6G scenarios through systematic numerical evaluations.

Findings

KD enhances lightweight AI model performance in distributed sensing tasks

Collaborative learning with KD reduces complexity while maintaining accuracy

Significant gains in multi-modal sensing-assisted beam tracking

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.