Federated Latent Transformer for Encrypted Codeword Compression
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Solution Overview
Problem
Existing data compression methods, particularly in satellite telemetry and command systems, fail to efficiently compress large datasets with low latency while preserving information integrity, necessitating a system that can reliably and efficiently compress data without loss.
Innovation Solution
A federated deep learning platform utilizing homomorphically-compressed and encrypted data, integrating neural networks to process encrypted codewords without decryption, supporting transformer and latent transformer architectures, and implementing differential privacy for secure collaborative learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional data compression methods are used, then compression ratio is improved, but information loss increases
Solution Approach 1:
The patent changes the fundamental parameter of compression from lossy approximation to lossless representation by using neural networks to learn exact data representations. The system transforms compression from reducing information to efficiently encoding information without loss, achieving both high compression ratios and perfect reconstruction through learned compression algorithms.
2Loss of information
If lossless compression algorithms are used, then information integrity is maintained, but compression efficiency deteriorates
Solution Approach 1:
The patent replaces traditional mechanical compression algorithms with neural network-based learned compression. Instead of using fixed mathematical transforms, the system learns optimal compression representations through training, enabling faster compression speeds while maintaining lossless reconstruction through adaptive learned models.
3Speed
If data is compressed without encryption, then processing speed is improved, but data security deteriorates
Solution Approach 1:
The patent introduces homomorphic encryption as an intermediary layer that enables processing of encrypted data without decryption. The neural networks operate on ciphertext, allowing compression and processing to occur while data remains encrypted, thus maintaining both security and processing capability through the encryption intermediary.
4Adaptability or versatility
If multiple client devices are integrated, then system versatility is improved, but system complexity increases
Solution Approach 1:
The patent segments the learning system into independent client devices that each run local neural network instances. Each client processes and compresses its own data locally, then shares only compressed representations or gradients with the central server, dividing the overall system complexity into manageable independent units while maintaining multi-client versatility.
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.