Latent Transformer Federated Learning with Encrypted Codewords
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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 datasets with low latency while preserving information integrity, especially in scenarios requiring secure and collaborative learning across multiple devices.
Innovation Solution
A federated deep learning platform utilizing homomorphically-compressed and encrypted data, employing a central server with a deep learning core that processes encrypted codewords from client devices, eliminating the need for embedding and positional encoding layers, and implementing differential privacy and adaptive optimization techniques to maintain data confidentiality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data compression methods are used in TT&C subsystems, then data transmission efficiency is improved, but information integrity and security are compromised
Solution Approach 1:
The compression system is divided into multiple independent neural network components: an encoder that compresses original data into compressed representations, and a decoder that reconstructs the original data from compressed form. This segmentation allows each component to specialize in its function while maintaining overall information integrity through the coordinated work of encoder and decoder.
Solution Approach 2:
The system creates compressed copies of the original data that can be transmitted efficiently while preserving all essential information. The decoder reconstructs the original data from these compressed copies, ensuring that the transmitted representation contains sufficient information to perfectly recreate the source data without loss.
2Quantity of substance
If lossy compression is applied to achieve higher compression ratios, then data size is reduced, but information loss occurs
Solution Approach 1:
The system transforms the data from its original high-dimensional form into a compressed parameter representation that captures all essential information in a more compact form. The neural network encoder learns optimal parameter transformations that preserve information while reducing data size, and the decoder reverses these transformations to reconstruct the original data perfectly.
3Productivity
If neural network-based compression is implemented, then compression efficiency is improved, but computational complexity and training requirements increase
Solution Approach 1:
The system performs comprehensive training of the encoder and decoder neural networks in advance, before actual compression and decompression operations. During this preliminary training phase, the networks learn optimal compression strategies and reconstruction methods. Once trained, the models can perform compression and decompression efficiently without requiring complex real-time computations, as the heavy learning work has already been completed beforehand.
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.