FlexP-SFL fine-tunes foundation models on resource-constrained devices through personalized split learning without parameter aggregation, improving accuracy and cutting wall-clock time and communication versus federated baselines.
Language models are unsupervised multitask learners
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.DC 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
Flexible Personalized Split Federated Learning for On-Device Fine-Tuning of Foundation Models
FlexP-SFL fine-tunes foundation models on resource-constrained devices through personalized split learning without parameter aggregation, improving accuracy and cutting wall-clock time and communication versus federated baselines.