FlexP-SFT: A Flexible Aggregation-Free Framework for On-Device Personalized Split Federated Fine-Tuning of LLMs
cs.DC, cs.LG
Submitted: 2025-08-14
Updated: 2026-09-01
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
- LLaMA: Open and Efficient Foundation Language Models
- GPT-4 Technical Report
- Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
- DeepSeek-V3 Technical Report
- On the Opportunities and Risks of Foundation Models
- Federated Learning: Opportunities and Challenges
- When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions
- Advances and Open Challenges in Federated Foundation Models
- Federated Learning with Personalization Layers
- Split learning for health: Distributed deep learning without sharing raw patient data
- End-to-End Evaluation of Federated Learning and Split Learning for Internet of Things
- FedMD: Heterogenous Federated Learning via Model Distillation
- Measuring Massive Multitask Language Understanding
- MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark
- Adaptive Personalized Federated Learning
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