Distributed Foundation Models for Heterogeneous Network Optimization
Dr. Mingzhe Chen, Prof. Walid Saad, Dr. Changchuan Yin
Virginia Tech / Beijing University of Posts and Telecommunications
Abstract
We introduce a distributed foundation model framework for optimizing heterogeneous wireless networks comprising macro cells, small cells, and Wi-Fi access points. A pre-trained base model is split across network tiers with tier-specific adapter modules, enabling coordinated optimization without sharing raw data between tiers. The framework achieves 30% better network-wide throughput than independent per-tier optimization while reducing inter-tier interference by 45%.
AI Summary
- Distributed foundation model framework for heterogeneous network optimization.
- 30% better network-wide throughput than per-tier independent optimization.
- 45% reduction in inter-tier interference through coordinated AI.
- No raw data sharing required between network tiers.
Key Findings
- 1Foundation model pre-training on diverse network data enables rapid adaptation to new deployments.
- 2Adapter modules add less than 5% parameter overhead per tier.
- 3Coordination through shared model representations outperforms explicit message-passing approaches.
Industry Implications
Provides a practical framework for AI-driven optimization in multi-tier 6G networks.
Addresses data privacy between different network operators and tiers.
Foundation model approach reduces the need for network-specific AI development.
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