6 papers
Open TeleDex: A Hardware-Agnostic Teleoperation System for Imitation Learning based Dexterous Manipulation
Xu Chi, Chao Zhang, Yang Su +7
Accurate and high-fidelity demonstration data acquisition is a critical bottleneck for deploying robot Imitation Learning (IL) systems, particularly when dealing with heterogeneous…
Communication-Aware Knowledge Distillation for Federated LLM Fine-Tuning over Wireless Networks
Xinlu Zhang, Na Yan, Yang Su +2
Federated learning (FL) for large language models (LLMs) offers a privacy-preserving scheme, enabling clients to collaboratively fine-tune locally deployed LLMs or smaller language…
HAFLQ: Heterogeneous Adaptive Federated LoRA Fine-tuned LLM with Quantization
Yang Su, Na Yan, Yansha Deng +2
Federated fine-tuning of pre-trained Large Language Models (LLMs) enables task-specific adaptation across diverse datasets while preserving privacy. However, challenges such as hig…
PWC-MoE: Privacy-Aware Wireless Collaborative Mixture of Experts
Yang Su, Na Yan, Yansha Deng +1
Large language models (LLMs) hosted on cloud servers alleviate the computational and storage burdens on local devices but raise privacy concerns due to sensitive data transmission…
Large-Scale AI in Telecom: Charting the Roadmap for Innovation, Scalability, and Enhanced Digital Experiences
Adnan Shahid, Adrian Kliks, Ahmed Al-Tahmeesschi +132
This white paper discusses the role of large-scale AI in the telecommunications industry, with a specific focus on the potential of generative AI to revolutionize network functions…
Federated Fine-Tuning of LLMs: Framework Comparison and Research Directions
Na Yan, Yang Su, Yansha Deng +1
Federated learning (FL) provides a privacy-preserving solution for fine-tuning pre-trained large language models (LLMs) using distributed private datasets, enabling task-specific a…