7 citations · 15 across the 18 of their papers we have counts for
4 papers · 1 filter
LLM DNA: Tracing Model Evolution via Functional Representations
Zhaomin Wu, Haodong Zhao, Ziyang Wang +3
The explosive growth of large language models (LLMs) has created a vast but opaque landscape: millions of models exist, yet their evolutionary relationships through fine-tuning, di…
FedRS-Bench: Realistic Federated Learning Datasets and Benchmarks in Remote Sensing
Haodong Zhao, Peng Peng, Chiyu Chen +2
Remote sensing (RS) images are usually produced at an unprecedented scale, yet they are geographically and institutionally distributed, making centralized model training challengin…
Vertical Federated Learning in Practice: The Good, the Bad, and the Ugly
Zhaomin Wu, Zhen Qin, Junyi Hou +4
Vertical Federated Learning (VFL) is a privacy-preserving collaborative learning paradigm that enables multiple parties with distinct feature sets to jointly train machine learning…
FedPrompt: Communication-Efficient and Privacy Preserving Prompt Tuning in Federated Learning
Haodong Zhao, Wei Du, Fangqi Li +2
Federated learning (FL) has enabled global model training on decentralized data in a privacy-preserving way by aggregating model updates. However, for many natural language process…