用轻量语言模型与压缩编码，实现移动网络预测的高效联邦学习。
Efficient Federated Learning Tiny Language Models for Mobile Network Feature Prediction
- 将微型语言模型融入联邦学习框架，提升网络特征预测能力。
- 采用NNCodec压缩技术，通信开销降至1%以下且性能几乎无损。
- 适合关注隐私保护与低带宽通信的智能移动网络研究者。
在电信领域，自治网络（ANs）根据特定需求（如带宽）和可用资源自动调整配置，依赖持续监控与智能机制实现自优化、自修复与自保护。如今，神经网络（NNs）被用于预测建模与模式识别。联邦学习（FL）允许多个网络单元协作训练模型，同时保护数据隐私。但传统FL需频繁传输大量神经网络参数，亟需高效的标准化压缩策略。为此，本文在新型联邦学习框架中引入了德国弗劳恩霍夫研究所开发的NNCodec（ISO/IEC神经网络编码标准实现），结合微型语言模型（TLMs）用于多种移动网络特征预测（如延迟、信噪比或频段频率）。在柏林车联网（Berlin V2X）数据集上的实验表明，NNCodec实现了透明压缩（即性能损失可忽略不计），并将通信开销降低至1%以下，验证了神经网络编码与联邦学习协同在联合学习自治移动网络中的有效性。
原文摘要 · Abstract (English)
In telecommunications, Autonomous Networks (ANs) automatically adjust configurations based on specific requirements (e.g., bandwidth) and available resources. These networks rely on continuous monitoring and intelligent mechanisms for self-optimization, self-repair, and self-protection, nowadays enhanced by Neural Networks (NNs) to enable predictive modeling and pattern recognition. Here, Federated Learning (FL) allows multiple AN cells - each equipped with NNs - to collaboratively train models while preserving data privacy. However, FL requires frequent transmission of large neural data and thus an efficient, standardized compression strategy for reliable communication. To address this, we investigate NNCodec, a Fraunhofer implementation of the ISO/IEC Neural Network Coding (NNC) standard, within a novel FL framework that integrates tiny language models (TLMs) for various mobile network feature prediction (e.g., ping, SNR or band frequency). Our experimental results on the Berlin V2X dataset demonstrate that NNCodec achieves transparent compression (i.e., negligible performance loss) while reducing communication overhead to below 1%, showing the effectiveness of combining NNC with FL in collaboratively learned autonomous mobile networks.
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