# Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges

arXiv: https://arxiv.org/abs/2507.16731 · HTML: https://mlsanity.com/paper/2507.16731/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jul 2025.

## Built on by (most cited first, 15 of 15)

- [Bridging On-Device and Cloud LLMs for Collaborative Reasoning: A Unified Methodology for Local Routing and Post-Training](https://mlsanity.com/paper/2509.24050/) (2025)
- [Hera: Learning Long-Horizon Coordination for Device-Cloud Collaborative LLM Agents](https://mlsanity.com/paper/2605.24598/) (2026)
- [PipeSD: An Efficient Cloud-Edge Collaborative Pipeline Inference Framework with Speculative Decoding](https://mlsanity.com/paper/2605.13319/) (2026)
- [Adapter-Augmented Bandits for Online Multi-Constrained Multi-Modal Inference Scheduling](https://mlsanity.com/paper/2603.06403/) (2026)
- [Federated Attention: A Distributed Paradigm for Collaborative LLM Inference over Edge Networks](https://mlsanity.com/paper/2511.02647/) (2025)
- [Federated Inference: Toward Privacy-Preserving Collaborative and Incentivized Model Serving](https://mlsanity.com/paper/2603.02214/) (2026)
- [ConsRoute:Consistency-Aware Adaptive Query Routing for Cloud-Edge-Device Large Language Models](https://mlsanity.com/paper/2603.21237/) (2026)
- [RailVQA: A Benchmark and Framework for Efficient Interpretable Visual Cognition in Automatic Train Operation](https://mlsanity.com/paper/2603.27112/) (2026)
- [PrivScope: Task-scoped Disclosure Control for Hybrid Agentic Systems](https://mlsanity.com/paper/2605.16630/) (2026)
- [An Efficient and Privacy-Preserving Architecture for Cross-Institutional Collaborative RAG](https://mlsanity.com/paper/2605.25716/) (2026)
- [Efficient and Privacy Aware Edge Cloud Collaborative Inference for Large Language Models](https://mlsanity.com/paper/2607.13093/) (2026)
- [PyroDash: Cost-Efficient Token-Level Small-Large Language Model Collaborative Inference](https://mlsanity.com/paper/2607.20327/) (2026)
- [CoE: Collaborative Entropy for Uncertainty Quantification in Agentic Multi-LLM Systems](https://mlsanity.com/paper/2603.28360/) (2026)
- [P2Skill: Privacy Preserving Skill Distillation for Cloud-Local LLM Inference Systems](https://mlsanity.com/paper/2608.14094/) (2026)
- [Empowering Edge Agents: A Systematic Analysis of Edge-Cloud Collaboration in LLM Agent Execution](https://mlsanity.com/paper/2610.17816/) (2026)

## Abstract (arXiv)

As large language models (LLMs) evolve, deploying them solely in the cloud or compressing them for edge devices has become inadequate due to concerns about latency, privacy, cost, and personalization. This survey explores a collaborative paradigm in which cloud-based LLMs and edge-deployed small language models (SLMs) cooperate across both inference and training. We present a unified taxonomy of edge-cloud collaboration strategies. For inference, we categorize approaches into task assignment, task division, and mixture-based collaboration at both task and token granularity, encompassing adaptive scheduling, resource-aware offloading, speculative decoding, and modular routing. For training, we review distributed adaptation techniques, including parameter alignment, pruning, bidirectional distillation, and small-model-guided optimization. We further summarize datasets, benchmarks, and deployment cases, and highlight privacy-preserving methods and vertical applications. This survey provides the first systematic foundation for LLM-SLM collaboration, bridging system and algorithm co-design to enable efficient, scalable, and trustworthy edge-cloud intelligence.