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cs.LG2026
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…
cs.LG2026
TextResNet: Decoupling and Routing Optimization Signals in Compound AI Systems via Deep Residual Tuning
Suizhi Huang, Mei Li, Han Yu +1
Textual Gradient-style optimizers (TextGrad) enable gradient-like feedback propagation through compound AI systems. However, they do not work well for deep chains. The root cause o…
cs.CL2026
Toward Federated Large Language Models in Medicine: A Parameter-Efficient Framework for Privacy-Preserving, Multi-Institutional Adaptation
Anran Li, Yuanyuan Chen, Wenjun Long +16
Large language models (LLMs) are increasingly adapted for medical applications, but most are trained using data from a single institution because privacy and governance constraints…