Fed-HeLLo allocates different LoRA layers to clients of different resource levels using importance scores and geometric patterns, improving federated fine-tuning accuracy over random allocation baselines.
Slimfit: Memory-efficient fine-tuning of transformer-based models using training dynamics
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Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation
Fed-HeLLo allocates different LoRA layers to clients of different resource levels using importance scores and geometric patterns, improving federated fine-tuning accuracy over random allocation baselines.