A server-side sequential training scheme with heterogeneous client-side LoRA submodels cuts server memory by 79% and training time by 6% versus a multi-model split federated baseline on BERT-base.
Holis tic network virtualization and pervasive network intelligenc e for 6G,
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Memory-Efficient Split Federated Learning for LLM Fine-Tuning on Heterogeneous Mobile Devices
A server-side sequential training scheme with heterogeneous client-side LoRA submodels cuts server memory by 79% and training time by 6% versus a multi-model split federated baseline on BERT-base.