most citedAdaptive Rank Allocation for Federated Parameter-Efficient Fine-Tuning of Language Models

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cs.LG2026

Preventing Rank Collapse in Federated Low-Rank Adaptation with Client Heterogeneity

Fei Wu, Jia Hu, Geyong Min +1

Federated low-rank adaptation (FedLoRA) has facilitated communication-efficient and privacy-preserving fine-tuning of foundation models for downstream tasks. In practical federated…

cs.LG2026

Efficient Orthogonal Fine-Tuning with Principal Subspace Adaptation

Fei Wu, Jia Hu, Geyong Min +1

Driven by the rapid growth of model parameters, parameter-efficient fine-tuning (PEFT) has become essential for adapting large models to diverse downstream tasks under constrained…

cs.LG2024

Federated Continual Learning for Edge-AI: A Comprehensive Survey

Zi Wang, Fei Wu, Feng Yu +3

Edge-AI, the convergence of edge computing and artificial intelligence (AI), has become a promising paradigm that enables the deployment of advanced AI models at the network edge,…