Publikation
Network Traffic Reduction Through AI-Assisted Local Data Optimization of Synthetic Data
Matthias Rüb; Jens Grüber; Hans Dieter Schotten
In: Proceedings of the The 8th International Conference on Information and Communications Technology 2025. International Conference on Information and Communications Technology (ICOIACT-2025), December 4, ISBN 979-8-3315-5408-8, IEEE, 2025.
Zusammenfassung
In future wireless networks, AI instances are expected
to take on autonomous tasks increasingly. In this context,
the availability of high-quality synthetic data is becoming more
important, as AI-agents in different domains, such as healthcare
and natural language processing (NLP) will be expected to
run safety and stability tests without the need for real data.
Synthetic data is also useful for clinical practitioners to address
imbalances in datasets or to augment existing data. However,
this increased demand for datasets places an additional burden
on the communication infrastructure, adding to the existing
traffic. This not only impacts the performance of the network
but also negatively affects sustainability. In this work addresses
these issues by introducing a new paradigm, which shifts away
from transmitting entire datasets and focuses on local data
generation and optimization instead. While this approach has
been desirable for a long time, recent developments in artificial
intelligence (AI), particularly in NLP, have made it now
feasible by empowering end-users without technical expertise
to perform data optimization. As an exemplary use case, this
work demonstrates a large language model (LLM) curated data
optimization using synthetic smart insole gait data. Several lowweight
LLMs are tasked to assist the curation. It is shown that
local light-weight models, which can be deployed even with lowcost
clinical IT infrastructure, can support non-experts with the
local optimization process of otherwise insufficient synthetic data.
Projekte
- SUSTAINET-guarDian - Sustainable Technologies for Advanced Resilient and Energy-Efficient Networks - guided utilities for automation, resilience, and Digital innovation in advanced networks
- IGEL-AI - IntelliGEnte Lösungen für Echtzeit Sicherheit: Methoden der Künstlichen Intelligenz zur Steigerung der Vertrauenswürdigkeit in mobilen Netzen