Federated Codeword Transformers for Private Low-Latency Compression
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Solution Overview
Problem
Existing data compression methods, particularly in telemetry, tracking, and command (TT&C) subsystems of satellite systems, face challenges in efficiently compressing large data sets without losing information, especially in scenarios requiring low-latency and high efficiency.
Innovation Solution
A federated deep learning platform utilizing homomorphically-compressed and encrypted data, integrating neural networks to convert local data into codewords, which are encrypted and processed by a central server without decryption, supporting Transformer and Latent Transformer architectures, and implementing differential privacy and adaptive optimization techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional lossless compression algorithms are used, then information integrity is preserved, but compression efficiency and processing speed deteriorate
Solution Approach 1:
The patent replaces traditional mechanical lossless compression algorithms with a neural network-based system. The compression network and decompression network use learned representations to achieve compression while maintaining information integrity, substituting deterministic algorithmic approaches with probabilistic deep learning models that can capture complex data patterns more efficiently.
Solution Approach 2:
The system changes the fundamental parameters of compression by using neural network architectures with learnable weights and biases. Instead of fixed compression rules, the system adapts parameters through training on data, allowing it to optimize compression efficiency for specific data types while preserving information through the learned transformation.
2Productivity
If data is compressed to reduce transmission size, then transmission efficiency improves, but data privacy and security worsen
Solution Approach 1:
The patent introduces homomorphic encryption as an intermediary layer between the compression network and the transmission channel. This allows the compressed data to remain encrypted throughout the process, enabling secure transmission while maintaining compression efficiency. The encryption acts as a mediator that preserves both privacy and transmission performance.
3Loss of information
If complex neural network architectures are used to improve compression, then compression quality improves, but computational complexity and training time worsen
Solution Approach 1:
The patent segments the compression system into two separate neural networks: a compression network that encodes data into a compressed representation, and a decompression network that reconstructs the original data. This segmentation allows each network to be optimized for its specific function, reducing overall computational complexity while maintaining compression quality through specialized architectures.
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.