# Beyond End-to-End: Dynamic Chain Optimization for Private LLM Adaptation on the Edge

arXiv: https://arxiv.org/abs/2604.06819 · HTML: https://mlsanity.com/paper/2604.06819/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Apr 2026.

## Built on by (most cited first, 3 of 3)

- [Spotlight and Shadow: Attention-Guided Dual-Anchor Introspective Decoding for MLLM Hallucination Mitigation](https://mlsanity.com/paper/2604.10071/) (2026)
- [FBLayout: Optimizing Memory Layout for Efficient LLM Finetuning on Mobile GPUs](https://mlsanity.com/paper/2607.21624/) (2026)
- [CoEM: Empowering Long-Context Reasoning with Commit-on-Evidence Memory](https://mlsanity.com/paper/2609.36935/) (2026)

## Abstract (arXiv)

Federated fine-tuning enables privacy-preserving LLM adaptation but faces a critical bottleneck: the disparity between LLMs' high memory demands and edge devices' limited capacity. To break the memory barrier, we propose Chain Federated Fine-Tuning (ChainFed), an innovative paradigm that forgoes end-to-end updates in favor of a sequential, layer-by-layer manner. It first trains the initial adapter to convergence, freezes its weights, and then proceeds to the next. This iterative train-and-freeze process forms an optimization chain, gradually enhancing the model's task-specific proficiency. ChainFed further integrates three core techniques: 1) Dynamic Layer Co-Tuning to bridge semantic gaps between sequentially tuned layers and facilitate information flow; 2) Globally Perceptive Optimization to endow each adapter with foresight beyond its local objective; 3) Function-Oriented Adaptive Tuning to automatically identify the optimal fine-tuning starting point. Extensive experiments on multiple benchmarks demonstrate the superiority of ChainFed over existing methods, boosting average accuracy by up to 46.46%.