[HTML][HTML] Large language models meet next-generation networking technologies: A review

CN Hang, PD Yu, R Morabito, CW Tan - Future Internet, 2024 - mdpi.com
The evolution of network technologies has significantly transformed global communication,
information sharing, and connectivity. Traditional networks, relying on static configurations …

When foundation model meets federated learning: Motivations, challenges, and future directions

W Zhuang, C Chen, L Lyu - arxiv preprint arxiv:2306.15546, 2023 - arxiv.org
The intersection of the Foundation Model (FM) and Federated Learning (FL) provides mutual
benefits, presents a unique opportunity to unlock new possibilities in AI research, and …

Open challenges and opportunities in federated foundation models towards biomedical healthcare

X Li, L Peng, YP Wang, W Zhang - BioData Mining, 2025 - Springer
This survey explores the transformative impact of foundation models (FMs) in artificial
intelligence, focusing on their integration with federated learning (FL) in biomedical …

Grounding foundation models through federated transfer learning: A general framework

Y Kang, T Fan, H Gu, X Zhang, L Fan… - arxiv preprint arxiv …, 2023 - arxiv.org
Foundation Models (FMs) such as GPT-4 encoded with vast knowledge and powerful
emergent abilities have achieved remarkable success in various natural language …

Fedra: A random allocation strategy for federated tuning to unleash the power of heterogeneous clients

S Su, B Li, X Xue - European Conference on Computer Vision, 2024 - Springer
With the increasing availability of Foundation Models, federated tuning has garnered
attention in the field of federated learning, utilizing data and computation resources from …

Iot in the era of generative ai: Vision and challenges

X Wang, Z Wan, A Hekmati, M Zong, S Alam… - arxiv preprint arxiv …, 2024 - arxiv.org
Equipped with sensing, networking, and computing capabilities, Internet of Things (IoT) such
as smartphones, wearables, smart speakers, and household robots have been seamlessly …

Automated federated pipeline for parameter-efficient fine-tuning of large language models

Z Fang, Z Lin, Z Chen, X Chen, Y Gao… - arxiv preprint arxiv …, 2024 - arxiv.org
Recently, there has been a surge in the development of advanced intelligent generative
content (AIGC), especially large language models (LLMs). However, for many downstream …

Artificial intelligence of things: A survey

SI Siam, H Ahn, L Liu, S Alam, H Shen, Z Cao… - ACM Transactions on …, 2025 - dl.acm.org
The integration of the Internet of Things (IoT) and modern Artificial Intelligence (AI) has given
rise to a new paradigm known as the Artificial Intelligence of Things (AIoT). In this survey, we …

[PDF][PDF] Backdoor threats from compromised foundation models to federated learning

X Li, S Wang, C Wu, H Zhou… - arxiv preprint arxiv …, 2023 - lixi1994.github.io
Federated learning (FL) represents a novel paradigm to machine learning, addressing
critical issues related to data privacy and security, yet suffering from data insufficiency and …

Towards Federated Large Language Models: Motivations, Methods, and Future Directions

Y Cheng, W Zhang, Z Zhang, C Zhang… - … Surveys & Tutorials, 2024 - ieeexplore.ieee.org
Large Language Models (LLMs), such as LLaMA and GPT-4, have transformed the
paradigm of natural language comprehension and generation. Despite their impressive …