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

ArXi:2602.13486v2 Announce Type: replace-cross Federated low-rank adaptation (FedLoRA) has facilitated communication-efficient and privacy-preserving fine-tuning of foundation models for downstream tasks. In practical federated learning scenarios, client heterogeneity in system resources and data distributions motivates the use of heterogeneous LoRA ranks across clients.