IP-Preserving Edge-Cloud Collaboration

• The paper demonstrates that maintaining raw assets locally while exchanging compressed representations supports cross-tier optimization without full disclosure.
• It introduces controlled interfaces such as embeddings, modular adapters, and latent handoffs to balance privacy with collaborative learning.
• Empirical results highlight significant performance gains and reduced communication costs compared to traditional federated and cloud-only approaches.

Across recent work, an “IP-preserving edge-cloud collaborative framework” can be understood as an edge-cloud arrangement in which the edge and the cloud cooperate without directly exchanging the raw assets each side treats as sensitive. Depending on the application, those assets may be private federated features, centralized historical features, full foundation-model weights, device-specific adaptation pipelines, or proprietary RTL code. The recurring systems pattern is to keep raw features, code, or full models local to their owners and to collaborate through narrower interfaces such as embeddings, prediction logits, modular sub-models, low-rank adapters, compressed latents, or abstract design principles. The literature therefore spans collaborative learning, collaborative inference, continual adaptation, multimodal foundation-model deployment, and hardware-code optimization, with differing degrees of explicitness in their privacy or intellectual-property claims (Li et al., 2023, Wang et al., 5 Aug 2025, Liu et al., 15 Feb 2026).

1. Conceptual scope and definitional boundaries

The topic does not refer to a single architecture class. In the current literature, some frameworks explicitly present themselves as privacy-preserving or IP-preserving, while others are better read as architectural precursors. The strongest explicit formulation appears in principle-guided Verilog optimization, which introduces “the first IP-preserving edge-cloud collaborative framework” for RTL optimization and enforces a hard constraint that the proprietary codebase $\mathcal{P}$ never becomes accessible to a cloud model (Wang et al., 5 Aug 2025). By contrast, ECCT is described as highly relevant “as a foundation for an IP-/privacy-preserving edge-cloud collaborative framework,” but it also states that stronger protections would require additional mechanisms beyond what it fully implements (Li et al., 2023). ECLM likewise is “highly relevant to an IP-preserving edge-cloud collaborative framework because the full model remains cloud-resident,” yet it does not claim to protect intellectual property formally (Zhuang et al., 2023). ML-ECS is framed as a “privacy-preserving deployment” paradigm for foundation models in which lightweight on-device models adapt to domain-specific data while cloud-hosted models coordinate knowledge sharing (Liu et al., 15 Feb 2026).

This distinction matters because “IP-preserving” is used in at least two senses. One concerns data and feature locality: raw user data, edge observations, or site-specific measurements remain local, while the cloud receives only derived artifacts. The other concerns model and code locality: the most valuable server-side model, routing policy, or proprietary codebase remains cloud- or on-premise-resident and is not fully disclosed to the edge. The literature supports both senses, but not always with formal confidentiality guarantees.

A second boundary concerns the unit of collaboration. Some systems collaborate at the level of representations and logits; some at the level of modules or adapters; some at the level of latents during generative inference; and some at the level of natural-language abstractions distilled from proprietary code. This suggests that the defining property is not any single algorithmic primitive, but a controlled information interface across the edge-cloud boundary.

2. Architectural principles and trust boundaries

A common architectural rule is locality by ownership: what belongs to the edge stays on the edge, and what belongs to the cloud stays in the cloud. In ECCT, device $k$ owns private federated-feature data

$$\hat{D}^k \coloneqq \left\{ \left( \hat{X}_i^k, y_i^k \right)\right\}_{i=1}^{N^k},$$

while the cloud stores centralized-feature data

$$D^k \coloneqq \left\{ \left( X_i^k, y_i^k \right)\right\}_{i=1}^{N^k}.$$

Raw private features $\hat{X}_i^k$ stay on the device, raw centralized features $X_i^k$ stay in the cloud, and the exchange consists of embeddings and logits rather than raw data or full parameters (Li et al., 2023). ECLM applies the same separation to model structure: the full large model stays in the cloud, while the edge receives only compact task-specific sub-models derived from block-level modular decomposition (Zhuang et al., 2023). ML-ECS keeps private multimodal datasets $D_j$ on edge devices and retains the stronger cloud LLM on the server, communicating only LoRA parameters and fused multimodal representations (Liu et al., 15 Feb 2026). Hybrid SD keeps the large diffusion model in the cloud and the compressed refinement model plus lightweight VAE on the edge, with a single handoff of latent state and text conditioning (Yan et al., 2024). Auto-Split similarly exposes only the front partition to the edge while leaving the suffix in the cloud (Banitalebi-Dehkordi et al., 2021).

The following representative patterns recur across the literature.

Framework	What remains local	What is exchanged
ECCT	edge raw private features; cloud raw centralized features; separate models	feature embeddings and prediction logits
ECLM	full modularized master model in cloud; personalized sub-model on edge	selected modules and updated module parameters
ML-ECS	private multimodal datasets on edge; full cloud LLM on server	LoRA parameters and fused multimodal representations
Hybrid SD	large SD model in cloud; small U-Net and lightweight VAE on edge	latent $4 \times 64 \times 64$ and text embeddings $77 \times 768$
Verilog principle-guided framework	proprietary codebase $\mathcal{P}$ on-premise	general design principles $k$0
Auto-Split	only model prefix on edge; suffix in cloud	quantized split-layer activations

These designs all narrow the trust boundary. They do not eliminate exposure, but they replace full disclosure with partial disclosure. A plausible implication is that IP preservation in edge-cloud systems is less about absolute secrecy than about choosing a collaboration interface whose semantics are useful enough for cross-tier optimization but too restricted to reveal the full asset.

3. Collaboration interfaces and optimization mechanisms

The literature offers several distinct collaboration primitives. In ECCT, the core mechanism is bi-directional knowledge transfer built from embedding fusion and knowledge distillation. The device computes

$k$1

the cloud computes

$k$2

and both models consume the concatenated representation $k$3. Training alternates between cloud optimization and device optimization, with KD terms on exchanged logits. This makes ECCT neither standard FedAvg nor pure split learning; it is more precisely an alternating edge-cloud co-training framework with representation sharing and mutual distillation (Li et al., 2023).

ECLM shifts the interface from representations to modular parameters. A large cloud model is decomposed into module layers and substitute modules, with top-$k$4 routing and a unified selector. Personalized sub-model extraction is then posed as a multidimensional knapsack problem over communication, computation, and memory budgets. The edge trains only the received modules, and the cloud reintegrates them by module-wise aggregation: $k$5 This achieves partial model disclosure by construction rather than by post hoc protection (Zhuang et al., 2023).

ML-ECS uses parameter-efficient multimodal collaboration. Its backbone update is

$k$6

with LoRA rank $k$7. On-device collaboration combines cross-modal contrastive learning, adaptive multimodal tuning, modality-aware aggregation, and SLM-enhanced server-side knowledge transfer. The server sends fused omni-modal representations computed on public data; devices return only LoRA parameters; and the server bridges client SLMs and the server LLM through KL-based knowledge transfer on pooled logits (Liu et al., 15 Feb 2026).

Hybrid SD uses a different interface altogether: time-step partitioning of the diffusion trajectory. A model-selection function $k$8 assigns early denoising steps to the cloud model and later steps to the edge model. The edge-cloud contract is therefore a latent handoff rather than a layerwise split. Auto-Split, by contrast, performs a classic DNN prefix-suffix split but jointly optimizes split point and edge-side mixed-precision quantization under latency, memory, and error constraints (Yan et al., 2024, Banitalebi-Dehkordi et al., 2021).

A further abstraction appears in cloud-edge CKM construction. There the problem is factorized as

$k$9

with the cloud learning a reusable foundation prior $$\hat{D}^k \coloneqq \left\{ \left( \hat{X}_i^k, y_i^k \right)\right\}_{i=1}^{N^k},$$0 once and the edge instantiating the task- and device-specific likelihood $$\hat{D}^k \coloneqq \left\{ \left( \hat{X}_i^k, y_i^k \right)\right\}_{i=1}^{N^k},$$1 locally at inference time. This moves collaboration from data exchange to prior sharing (Xiao et al., 7 Feb 2026).

4. Representative framework families and application domains

In feature-partitioned learning, ECCT is the canonical example. It targets settings such as recommender systems where the cloud already owns historical interactions, item metadata, and other non-private features, while devices own privacy-sensitive user attributes and real-time signals. The framework supports personalization, model heterogeneity, training asynchronization, and lower communication burden because it exchanges embeddings and logits rather than full parameters (Li et al., 2023).

In continual edge adaptation, ECLM addresses dynamic edge environments with frequent data-distribution shifts and on-device resource fluctuations. Its contribution is to turn a large model into a library of reusable modules and to let the cloud periodically provide up-to-date personalized sub-models while reintegrating only updated modules from the edge. This supports task-specific disclosure rather than monolithic model shipment (Zhuang et al., 2023).

In multimodal foundation-model deployment, ML-ECS addresses modality heterogeneity and model-structure heterogeneity across clients. The edge uses an SLM backbone and local private data; the server uses a stronger LLM backbone and public omni-modal data. Collaboration is therefore not just federated averaging but multimodal latent alignment plus server-side teacher-student transfer (Liu et al., 15 Feb 2026).

In generative inference, Hybrid SD assigns semantic planning to the cloud and fidelity-improving denoising to the edge. The edge receives a latent of size $$\hat{D}^k \coloneqq \left\{ \left( \hat{X}_i^k, y_i^k \right)\right\}_{i=1}^{N^k},$$2 and text embeddings of size $$\hat{D}^k \coloneqq \left\{ \left( \hat{X}_i^k, y_i^k \right)\right\}_{i=1}^{N^k},$$3, a payload reported as 148 KB in FP16. This suggests a design in which the most capable generative core can remain server-side while later refinement and final decoding occur locally (Yan et al., 2024).

In code and design optimization, the Verilog framework is the most explicit IP-preserving instance. It defines a proprietary codebase $$\hat{D}^k \coloneqq \left\{ \left( \hat{X}_i^k, y_i^k \right)\right\}_{i=1}^{N^k},$$4, a draft codebase $$\hat{D}^k \coloneqq \left\{ \left( \hat{X}_i^k, y_i^k \right)\right\}_{i=1}^{N^k},$$5, a principle extraction stage

$$\hat{D}^k \coloneqq \left\{ \left( \hat{X}_i^k, y_i^k \right)\right\}_{i=1}^{N^k},$$6

and a cloud optimization stage

$$\hat{D}^k \coloneqq \left\{ \left( \hat{X}_i^k, y_i^k \right)\right\}_{i=1}^{N^k},$$7

Here the collaboration interface is neither weights nor activations but abstract design principles, which the paper treats as “IP-safe” guidance (Wang et al., 5 Aug 2025).

Adjacent systems reinforce the same motif even when they do not explicitly claim IP preservation. LAECIPS keeps the strongest vision model in the cloud and uploads only hard inputs, while EcoAgent performs local screen understanding and sends compact textual summaries to the cloud planning agent. This suggests a broader pattern in which the edge acts as a privacy-minimizing or IP-minimizing abstraction layer rather than a mere sensor front-end (Hu et al., 2024, Yi et al., 8 May 2025).

5. Empirical evidence and observed system trade-offs

The empirical record shows that controlled interfaces can preserve substantial utility. In ECCT, collaborative learning on separated feature ownership outperforms federated-only baselines on both image and recommendation tasks. On Avazu CTR prediction, ECCT on the collaborative split achieves 0.6694 AUC, compared with 0.5783 for FedAvg on federated-only features and 0.6595 for FedAvg with all cloud features moved to the edge. On IndustryData, ECCT achieves 0.6885 AUC / 0.0875 MSE for CTR and 0.8754 AUC / 0.0184 MSE for CVR. In asynchronous CIFAR10 experiments, synchronized ECCT reaches 67.81 versus 62.69 for FedAvg, and with 50% selection ratio synchronized ECCT reaches 69.28 while FedAvg drops to 55.78 (Li et al., 2023).

ECLM reports gains centered on adaptation efficiency under non-IID and dynamic conditions. Across tasks, it claims up to 18.89% accuracy increase and up to $$\hat{D}^k \coloneqq \left\{ \left( \hat{X}_i^k, y_i^k \right)\right\}_{i=1}^{N^k},$$8 communication cost reduction. It reports average communication-cost reduction of $$\hat{D}^k \coloneqq \left\{ \left( \hat{X}_i^k, y_i^k \right)\right\}_{i=1}^{N^k},$$9 versus FedAvg and $$D^k \coloneqq \left\{ \left( X_i^k, y_i^k \right)\right\}_{i=1}^{N^k}.$$0 versus HeteroFL, with memory footprint and per-batch training latency reduced by up to $$D^k \coloneqq \left\{ \left( X_i^k, y_i^k \right)\right\}_{i=1}^{N^k}.$$1 and $$D^k \coloneqq \left\{ \left( X_i^k, y_i^k \right)\right\}_{i=1}^{N^k}.$$2, respectively. In continuous adaptation over 100 steps with 50% data replacement per step, it reduces average adaptation time by 14.5%, 45.5%, 63.5%, and 75.3% on the four tasks (Zhuang et al., 2023).

ML-ECS reports that it communicates only 0.65% of the total parameter volume while improving Rouge-LSum by 5.44% to 12.08% and BERTScore by 0.72% to 9.23% relative to Standalone. Hybrid SD reports compressed edge models with 225.8M parameters and a collaborative configuration that reduces cloud cost by 66%. Auto-Split reports latency reductions of 20–80% vs QDMP, 24–92% vs Neurosurgeon, and 32–92% vs Cloud-Only whenever it does not choose Cloud-Only; in its license-plate-recognition case study, Auto-Split achieves 630 ms latency with 15 MB edge size, versus 970 ms for cloud-only and 2840 ms for full 8-bit on-edge inference (Liu et al., 15 Feb 2026, Yan et al., 2024, Banitalebi-Dehkordi et al., 2021).

The most explicit IP-preserving results appear in Verilog optimization. For power optimization, Qwen-2.5-Coder-7B + DeepSeek-V3 achieves a 66.67\% success rate, outperforming DeepSeek-V3 alone (49.81\%) and GPT-4o (55.81\%); for critical path delay, Qwen2.5-Instruct-7B + DeepSeek-V3 reaches 50.85\%, compared with 33.90\% for standalone DeepSeek-V3 direct prompting (Wang et al., 5 Aug 2025).

6. Security limits, leakage surfaces, and unresolved issues

A recurrent misconception is that edge-cloud collaboration is automatically privacy-preserving or IP-preserving. The literature does not support that stronger claim. ECCT explicitly states that “the transfer of embeddings bears less privacy risk than directly transferring the device’s raw private features,” but it does not provide a formal leakage proof, reconstruction analysis, or secure-channel design; it only suggests that privacy can be further strengthened by applying differential privacy to device embeddings (Li et al., 2023). ECLM is similar: it limits disclosure by sending only sub-models, but it does not include encryption, secure aggregation, watermarking, TEE use, access control, or anti-harvesting rate limits (Zhuang et al., 2023).

The same limitation appears in more explicitly IP-oriented systems. The Verilog framework relies on abstraction-by-summarization: proprietary RTL remains local, and only general design principles are sent to the cloud. Yet the paper does not provide a formal privacy guarantee, information-theoretic leakage bound, or inversion analysis of those principles (Wang et al., 5 Aug 2025). ML-ECS keeps raw private multimodal data local and communicates only LoRA parameters and fused representations, but it does not establish differential privacy, cryptographic confidentiality, or resistance to model inversion or membership inference (Liu et al., 15 Feb 2026). Hybrid SD avoids direct upload of some user data and keeps the large model server-side, but it still transfers latent state and text embeddings, and it does not analyze inversion or leakage from those intermediates (Yan et al., 2024).

This leaves a consistent open problem. The systems and learning blueprint is already strong: keep raw data local, keep proprietary full models local, and collaborate through compressed semantic interfaces. What remains underdeveloped is the security theory and mechanism layer around that interface: leakage-aware representation learning, secure aggregation, authenticated access control, model watermarking, anti-extraction defenses, trusted execution, transport protection, and explicit adversary models. The literature therefore supports a clear conclusion: edge-cloud collaboration has become a practical substrate for privacy- and IP-aware deployment, but only some frameworks make IP preservation an explicit objective, and even those generally stop at architectural separation rather than formal confidentiality guarantees.