End-Edge Collaborative Generation Framework

• End-Edge Collaborative Generation-Enhancement Frameworks are distributed generative AI architectures that partition tasks between resource-constrained end devices and powerful edge servers to optimize quality, latency, and bandwidth.
• They employ advanced techniques such as model splitting, seed/sketch-based communication, and adaptive resource allocation to ensure robust performance under constraints like low SNR and limited bandwidth.
• Empirical evaluations show up to 46× bandwidth savings and high image quality (e.g., PSNR >25 dB) in mobile edge deployments across text, image, video, and audio tasks.

End-Edge Collaborative Generation-Enhancement Frameworks (EECGEFs) represent a class of distributed generative AI architectures in which generative models are strategically partitioned between resource-constrained end devices (e.g., user equipment, mobile terminals) and more powerful edge servers. These frameworks leverage model splitting, communication-efficient information exchange (seeds, sketches, features), cooperative processing, and adaptive protocol design to jointly optimize for quality, latency, and communication overhead under real-world system constraints—including low signal-to-noise ratios, bandwidth limits, and heterogeneous computational capacities. EECGEFs are foundational to next-generation mobile edge intelligence, enabling robust, low-latency generative services in future 6G and beyond wireless systems (Zhong et al., 2023).

1. Architectural Paradigms and Deployment Schemes

Fundamental EECGEFs are organized according to model partitioning, allocation of functional roles (inference, generation, sketching, completion), and the structure of information exchange. The “Mobile Edge Generation” (MEG) framework (Zhong et al., 2023), for instance, formalizes:

Single-Edge Frameworks (one-to-one ES–UE joint generation):

• Seed-based generation: The “inferencer” (at ES or UE) derives a compact seed $s$ from request $x$. $s$ is transmitted to the other party, where the “generator” synthesizes the output $y$.
• Sketch-based generation: The “sketcher” produces a low-res or outline sketch $k$, which is transmitted and refined by the “completer” into $y$.

Task allocation protocols comprise:

• UIEG (UE-Inference & ES-Generation): Inferencer at UE, generator at ES.
• EIUG (ES-Inference & UE-Generation): Inferencer at ES, generator at UE.
• CIAG (Cooperative Inference & Generation): UE inferencer, ES seed-to-seed generator, UE generator.
• ESUC (Edge Sketch & UE Complete): ES sketcher, UE completer.

Multi-Edge Frameworks (many ESs → one UE):

Support parallel or cooperative execution:

• Parallel: UIDG, DIUG, DSUC—each ES works independently; user selects/merges outputs.
• Cooperative: UIDCG, DCSUC—seeds/sketches partitioned across ESs, outputs recombined at UE.

Model partitioning is exemplified by latent diffusion pipelines: e.g., encoder at UE, U-Net and decoder at ES (UIEG); or intermediate splits (CIAG), with layers allocated according to resource and communication constraints (Zhong et al., 2023).

2. Mathematical Formalization and System Constraints

EECGEFs rigorously express design trade-offs via constrained optimization:

Quality Maximization under Budget:

$$\max_{w_{ES},w_{UE}} Q(w_{ES},w_{UE}) \quad \text{s.t.} \;\; \mathcal{C}(w_{ES},w_{UE})\leq B,\;\; T(w_{ES},w_{UE})\leq L$$

where $Q$ is generative quality, $\mathcal{C}$ is communication overhead, $B$ is bandwidth, $T$ is total latency, and $L$ is a latency bound.

Communication cost model:

$\mathcal{C} = \alpha S_{sketch} + \beta S_{seed}$

with $\alpha$, $\beta$ characterizing modulation/coding for sketch and seed bitrates.

Latency decomposition:

$T = t_{UE \to ES} + t_{compute} + t_{ES \to UE}$

$t_{compute}$ includes all partitioned local and remote inference/generation steps.

Low SNR robustness: Output distortion ($D(\gamma)$) is linked to standard metrics: $$\mathrm{PSNR}(\gamma) = 10\log_{10} \left( \frac{\max(y)^2}{\mathbb{E} \|y - \hat{y}\|^2} \right)$$

$$\mathrm{FID}(\gamma) \approx \|\mu_r - \mu_g(\gamma)\|^2 + \mathrm{Tr}\left(\Sigma_r + \Sigma_g(\gamma) - 2(\Sigma_r \Sigma_g(\gamma))^{1/2}\right)$$

where $\hat{y}$ is reconstructed at SNR $\gamma$, and $y$ is the reference output.

3. Protocol Mechanics and Modalities

Concrete protocol realizations use:

• Seed/sketch-based protocols: Deterministically transmit compact intermediate representations (see UIEG, EIUG, ESUC); optimize for efficient delivery and noise resilience (Zhong et al., 2023).
• Cooperative multi-edge protocols: Partition seeds/sketches, distribute across $n$ ESs, aggregate multiple partial generations.
• Model partitioning strategies for diffusion models: Allocate initial layers (e.g., encoders) to UE, heavy diffusion cores to ES, and final decoders back to UE.

Pseudocode example (UIEG, seed-based):

1. UE: $s \gets$ Inferencer$_{\text{UE}}(x)$; send $s$
2. ES: $\hat{s} = s +$ noise; $y \gets$ Generator$_{\text{ES}}(\hat{s})$; send $y$
3. UE: receive $\hat{y}$ and display

These mechanisms have been instantiated with latent diffusion models and validated for image-to-image and text-to-image tasks (Zhong et al., 2023).

Generalizations extend to:

• Video: Partition keyframe encoder (@UE), long-term diffusion (@ES), decoder (@UE), transmit motion seeds.
• Audio: Use Mel-spectrogram encoders (@UE), waveform generators (@ES), local postprocessing.
• Text: Prompt encoder (@UE), transformer layers (@ES), final language head (@UE).

4. Performance Evaluation and Empirical Results

Case studies on text-guided image-to-image pipelines (LDM architecture) demonstrate:

Protocol	Communication Overhead per 256×256 img+txt (kb)	PSNR at −20 dB SNR
Full-UE/ES	~1,300	–
UIEG/EIUG	~28	>25 dB
CIAG	~57	>25 dB
ESUC	~120	–

• Bandwidth savings: Up to $46\times$ reduction (1.3 Mb $\to$ 28 kb).
• Visual robustness: CIAG/EIUG maintain high detail even at −20 dB SNR; UIEG/EIUG incur minor pixel distortion.
• PSNR decay: CIAG degrades gently vs. SNR; achieves PSNR >25 dB at lowest tested SNR.
• Latency: Trade-off governed by layer allocation; more UE compute reduces uplink payload, more ES compute simplifies UE but increases network usage (Zhong et al., 2023).

5. Enhancement, Quality, and Adaptivity Mechanisms

Robustness and quality enhancement are achieved through:

• Robust encoding: Channel coding, unequal error protection, and oversampling for seeds/sketches.
• Adaptive resource allocation: Dynamic bit allocation to regions of interest (for sketches), and over-sampling/denoising for latent features.
• Model split optimization: Solve for weights $w_{ES},w_{UE}$ to minimize $\lambda\, C + \mu\, T$ subject to $Q \geq Q_{\min}$, where $C$ and $T$ are communication and latency.
• Edge-complexity trade-off: Shifting model depth or component size between UE and ES for optimal joint efficiency (Zhong et al., 2023).
• Generalization: Framework is extendable from images to other data modalities (video, audio, text), as long as corresponding encoders/generators can be modularized.

6. Comparative Protocol Analysis and Modal Scope

Distinct task allocation protocols enable flexible system architectures. The single-ES protocols (UIEG, EIUG, CIAG, ESUC) enable one-to-one division along inference-generation and sketch-completion axes, while multi-ES schemes support parallelization and cooperative generation—harnessing multiple edge resources for reduced latency and increased resilience. Criteria for protocol choice include available bandwidth, computational resources, latency constraints, and noise environment. Multi-ES schemes further provide user-side selection, merging, or aggregation of generated outputs from several ESs (Zhong et al., 2023).

Adaptive partitioning enables the framework to be domain- and modality-agnostic, supporting deployment in diverse 6G mobile and IoT scenarios.

7. Synthesis and Outlook

The taxonomy of end-edge collaborative generation-enhancement frameworks, grounded in real-world system constraints and rigorously assessed against SNR, bandwidth, and latency limits, provides the technical foundation for scalable wireless generative AI. Model modularization, efficient intermediate (seed/sketch) transmission, and dynamic protocol selection collectively yield orders-of-magnitude communication savings and high-quality, low-latency generation, even in harsh environments.

These frameworks anticipate a broad class of generative workflows—text, image, video, and beyond—defining a canonical approach for resource-efficient, high-fidelity generative intelligence at the network edge (Zhong et al., 2023).