Split Variational Autoencoder for Adaptive Edge-Cloud Compression
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing deep learning approaches for data compression and restoration lack efficiency in distributed processing across edge computing devices and cloud infrastructure, particularly in adapting to varying resource availability and network conditions.
Innovation Solution
A split variational autoencoder architecture is implemented, spanning edge devices and cloud infrastructure, which includes resource monitoring, dynamic compression parameter adjustment, and a distributed latent space vector correlator to enhance reconstruction quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data compression is performed using deep learning approaches, then data transmission efficiency is improved, but computational resource consumption increases
Solution Approach 1:
The patent divides the autoencoder architecture into two segments: encoder deployed at edge devices for local compression, and decoder deployed at cloud servers for decompression. This segmentation allows computationally intensive encoding operations to be performed at the edge with limited resources, while leveraging cloud resources for decoding, thus resolving the contradiction between compression efficiency and resource consumption.
Solution Approach 2:
The patent transforms the data from high-dimensional raw format to low-dimensional latent space representation through the encoder, and then reconstructs it back to high-dimensional format at the cloud. This dimensional transformation enables efficient compression by representing data in a compact latent form that captures essential features while reducing computational burden for transmission and processing.
2Adaptability or versatility
If compression parameters are adjusted dynamically, then adaptability to network conditions is improved, but system complexity increases
Solution Approach 1:
The patent implements a feedback mechanism where the system continuously monitors network conditions (bandwidth, latency, packet loss) and dynamically adjusts compression parameters (compression ratio, latent space dimensionality) accordingly. This feedback loop enables the system to adapt to changing network conditions automatically, optimizing transmission efficiency while maintaining acceptable reconstruction quality without manual intervention.
3Productivity
If distributed processing is implemented across edge-cloud architecture, then processing scalability is improved, but coordination overhead increases
Solution Approach 1:
The patent introduces an intermediary component that manages the coordination between edge devices and cloud servers. This intermediary handles model distribution, parameter synchronization, and result aggregation, thereby simplifying the coordination overhead inherent in distributed processing while maintaining the scalability benefits of the edge-cloud architecture.
Data Source
AI summary
A distributed system and method for compressing and restoring data across edge computing devices and cloud infrastructure is disclosed. The system preprocesses raw data at edge computing devices, compresses the data into latent space vectors using distributed encoders within a variational autoencoder spanning edge and cloud components, decompresses the vectors using decoders, and processes them through a resource-aware neural upsampler to generate enhanced reconstructed outputs. The system dynamically adapts compression based on available computing resources and network conditions, while enabling secure distributed processing through homomorphic operations on compressed data. Edge-cloud coordination layers manage data flow, compression parameters, and workload distribution, while maintaining system reliability through intelligent failover handling and resource optimization.