Latent Transformer Compression on Encrypted Codewords
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Solution Overview
Problem
Existing data compression methods, particularly in telemetry, tracking, and command (TT&C) subsystems for satellite systems, face challenges in efficiently compressing large datasets without losing information, especially with low-latency requirements.
Innovation Solution
A federated deep learning platform using homomorphically-compressed and encrypted data, integrating neural networks, supports transformer-based architectures that process encrypted codewords without decryption, enabling collaborative learning while maintaining data privacy through differential privacy and adaptive optimization techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional compression algorithms are used, then compression speed is improved, but information loss occurs
Solution Approach 1:
The patent replaces traditional mechanical compression algorithms with a neural network-based learning system. The neural network learns compression patterns from training data and applies learned transformations to compress new data while preserving information through the learned representation, eliminating the information loss inherent in traditional lossy compression methods.
Solution Approach 2:
The patent transforms data into a different parameter space (latent space) where compression occurs naturally through dimensionality reduction and pattern recognition. By changing the representation parameters rather than applying fixed compression transformations, the system achieves both high compression ratios and information preservation simultaneously.
2Loss of information
If lossless compression is used to preserve information, then information integrity is improved, but compression efficiency deteriorates
Solution Approach 1:
The patent performs preliminary training of the neural network on large datasets to learn optimal compression representations before actual compression occurs. This preliminary learning phase enables the system to achieve both lossless compression and high efficiency during the actual compression operation, as the network has already learned the most effective compression patterns.
Solution Approach 2:
The patent replaces inefficient traditional lossless compression algorithms with a neural network-based system that learns optimal compression strategies. The neural network processes data through learned transformations that achieve both complete information preservation and superior compression ratios compared to traditional methods.
3Reliability
If data is encrypted before compression, then security is improved, but processing complexity increases
Solution Approach 1:
The patent performs encryption as a preliminary step before compression, and the neural network is specifically trained to handle encrypted data. This preliminary encryption followed by specialized neural network processing achieves both security and efficiency, as the network learns to compress patterns in encrypted data without requiring decryption.
Solution Approach 2:
The neural network acts as an intermediary that processes encrypted data directly without requiring decryption. This intermediary system bridges the gap between security requirements (encrypted data) and compression requirements (processable data), enabling both security and efficiency simultaneously.
Data Source
AI summary
A system and method for a federated deep learning platform utilizing homomorphically-compressed and encrypted data. The system comprises multiple client devices, each with a local dataset, and a central server hosting a deep learning core. Client devices convert local data into codewords, which are also homomorphically encrypted. The central server processes these encrypted codewords without decryption, preserving data privacy. The platform supports at least two architectural variants: a conventional Transformer trained on codewords, and a Latent Transformer operating on latent space vectors. Both variants eliminate the need for embedding and positional encoding layers. The system aggregates encrypted model updates from clients, enabling collaborative learning while maintaining data confidentiality. Additional features comprise differential privacy implementation and adaptive federated optimization techniques. This innovative approach allows for efficient, privacy-preserving distributed learning across diverse datasets, addressing key challenges in federated learning such as data heterogeneity, non-IID distributions, and communication efficiency.