198 citations · 311 across the 15 of their papers we have counts for
8 papers · 1 filter
Towards CSI-Native Foundation Models: A Channel-Adaptive Roadmap for 6G
Chenyu Zhang, Xinchen Lyu, Chenshan Ren +2
Wireless foundation models offer a path toward reusable channel state information (CSI) intelligence for sixth-generation (6G) systems. However, existing generic-backbone adaptatio…
HeterCSI: Channel-Adaptive Heterogeneous CSI Pretraining Framework for Generalized Wireless Foundation Models
Chenyu Zhang, Xinchen Lyu, Chenshan Ren +3
Wireless foundation models promise transformative capabilities for channel state information (CSI) processing across diverse 6G network applications, yet face fundamental challenge…
SplitFrozen: Split Learning with Device-side Model Frozen for Fine-Tuning LLM on Heterogeneous Resource-Constrained Devices
Jian Ma, Xinchen Lyu, Jun Jiang +3
Fine-tuning large language models (LLMs) on private, on-device data can empower tailored personalized AI agents. However, fine-tuning LLMs on resource-constrained edge devices face…
A Unified Analysis of Stochastic Gradient Descent with Arbitrary Data Permutations and Beyond
Yipeng Li, Xinchen Lyu, Zhenyu Liu
We aim to provide a unified convergence analysis for permutation-based Stochastic Gradient Descent (SGD), where data examples are permuted before each epoch. By examining the relat…
Sharp Bounds for Sequential Federated Learning on Heterogeneous Data
Yipeng Li, Xinchen Lyu
There are two paradigms in Federated Learning (FL): parallel FL (PFL), where models are trained in a parallel manner across clients, and sequential FL (SFL), where models are train…
Convergence Analysis of Sequential Federated Learning on Heterogeneous Data
Yipeng Li, Xinchen Lyu
There are two categories of methods in Federated Learning (FL) for joint training across multiple clients: (i) parallel FL (PFL), where clients train models in a parallel manner; a…
Convergence Analysis of Sequential Split Learning on Heterogeneous Data
Yipeng Li, Xinchen Lyu
Federated Learning (FL) and Split Learning (SL) are two popular paradigms of distributed machine learning. By offloading the computation-intensive portions to the server, SL is pro…
Similarity-based Label Inference Attack against Training and Inference of Split Learning
Junlin Liu, Xinchen Lyu, Qimei Cui +1
Split learning is a promising paradigm for privacy-preserving distributed learning. The learning model can be cut into multiple portions to be collaboratively trained at the partic…