7 papers · 1 filter
A Survey on Federated Causal Discovery and Inference
Xianjie Guo, Yuwei Wang, Guodu Xiang +4
Causal reasoning, which encompasses the discovery of causal structures and the inference of causal effects, is fundamental to data-driven decision making. In practice, data for rel…
Rethinking LoRA for Data Heterogeneous Federated Learning: Subspace and State Alignment
Hongyi Peng, Han Yu, Xiaoxiao Li +1
Low-Rank Adaptation (LoRA) is widely used for federated fine-tuning. Yet under non-IID settings, it can substantially underperform full-parameter fine-tuning. Through with-high-pro…
Can Textual Gradient Work in Federated Learning?
Minghui Chen, Ruinan Jin, Wenlong Deng +4
Recent studies highlight the promise of LLM-based prompt optimization, especially with TextGrad, which automates differentiation'' via texts and backpropagates textual feedback. Th…
Advances and Open Challenges in Federated Foundation Models
Chao Ren, Han Yu, Hongyi Peng +9
The integration of Foundation Models (FMs) with Federated Learning (FL) presents a transformative paradigm in Artificial Intelligence (AI). This integration offers enhanced capabil…
Federated Model Heterogeneous Matryoshka Representation Learning
Liping Yi, Han Yu, Chao Ren +3
Model heterogeneous federated learning (MHeteroFL) enables FL clients to collaboratively train models with heterogeneous structures in a distributed fashion. However, existing MHet…
pFedAFM: Adaptive Feature Mixture for Batch-Level Personalization in Heterogeneous Federated Learning
Liping Yi, Han Yu, Chao Ren +4
Model-heterogeneous personalized federated learning (MHPFL) enables FL clients to train structurally different personalized models on non-independent and identically distributed (n…