6 papers
Optimizing Server Placement for Vertical Federated Learning in Dynamic Edge/Fog Networks
Su Wang, Mung Chiang, H. Vincent Poor
We investigate the control and optimization of vertical federated learning (VFL), a class of distributed machine learning (ML) methods in which edge/fog devices contain separate da…
Large Language Models over Networks: Collaborative Intelligence under Resource Constraints
Liangqi Yuan, Wenzhi Fang, Shiqiang Wang +2
Large language models (LLMs) are transforming society, powering applications from smartphone assistants to autonomous driving. Yet cloud-based LLM services alone cannot serve a gro…
A New Pathway to Integrated Learning and Communication (ILAC): Large AI Model and Hyperdimensional Computing for Communication
Wei Xu, Zhaohui Yang, Derrick Wing Kwan Ng +4
The rapid evolution of forthcoming sixth-generation (6G) wireless networks necessitates the seamless integration of artificial intelligence (AI) with wireless communications to sup…
LLM-Integrated Digital Twins for Hierarchical Resource Allocation in 6G Networks
Majumder Haider, Imtiaz Ahmed, Zoheb Hassan +2
Next-generation (NextG) wireless networks are expected to require intelligent, scalable, and context-aware radio resource management (RRM) to support ultra-dense deployments, diver…
Route-and-Aggregate Decentralized Federated Learning Under Communication Errors
Weicai Li, Tiejun Lv, Wei Ni +3
Decentralized federated learning (D-FL) allows clients to aggregate learning models locally, offering flexibility and scalability. Existing D-FL methods use gossip protocols, which…
A MIMO ISAC System for Ultra-Reliable and Low-Latency Communications
Homa Nikbakht, Yonina C. Eldar, H. Vincent Poor
In this paper, we propose a bi-static multiple-input multiple-output (MIMO) integrated sensing and communication (ISAC) system to detect the arrival of ultra-reliable and low-laten…