2024
Thinking Forward: Memory-Efficient Federated Finetuning of Language Models
NeurIPS 2024poster
Finetuning large language models (LLMs) in federated learning (FL) settings has become increasingly important as it allows resource-constrained devices to finetune a model using private data. However, finetuning LLMs using backpropagation requires excessive memory (especially from intermediate activ…