FedSmoothLoRA improves federated LoRA fine-tuning by constructing local initializations from a round-matching matrix for cross-round continuity and a gradient-aligned matrix for client-specific guidance, yielding faster convergence than prior methods in image and text tasks.
Federa: Efficient fine-tuning of language models in federated learning leveraging weight decomposition
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
Proposes and benchmarks a new aggregation technique for LoRA adapters in federated fine-tuning against existing methods on GLUE tasks.
citing papers explorer
-
Aggregating Low Rank Adapters in Federated Fine-tuning
Proposes and benchmarks a new aggregation technique for LoRA adapters in federated fine-tuning against existing methods on GLUE tasks.