Homomorphic Codeword Compression for Privacy-Preserving Federated Learning
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Solution Overview
Problem
Current data compression methods struggle to efficiently compress large sets of data without losing information, particularly in applications like telemetry, tracking, and command (TT&C) subsystems for satellite systems.
Innovation Solution
A federated deep learning platform utilizing homomorphically-compressed and encrypted data, which includes multiple client devices and a central server hosting a deep learning core. Client devices convert local data into codewords that are also homomorphically encrypted, allowing the central server to process these encrypted codewords without decryption, preserving data privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If lossless compression algorithms are used to preserve all original information, then information integrity is maintained, but compression efficiency and processing speed deteriorate
Solution Approach 1:
The patent introduces an intermediary neural network compression system that acts as a mediator between the original data and the compressed representation. The encoder neural network transforms input data into a compressed form while preserving essential information, and the decoder neural network reconstructs the original data from the compressed form. This intermediary system resolves the contradiction by achieving both high compression ratios and lossless reconstruction through learned transformations rather than traditional compression algorithms.
Solution Approach 2:
The patent changes the parameters of the data representation by transforming it through neural network layers that learn optimal compression parameters. The encoder network adjusts parameters such as feature dimensions, activation functions, and layer configurations to achieve efficient compression. The system dynamically optimizes these parameters during training to balance compression efficiency with information preservation, resolving the contradiction between compression ratio and reconstruction quality.
2Quantity of substance
If traditional compression methods are applied to large datasets, then data size is reduced, but processing time and computational complexity increase
Solution Approach 1:
The patent applies preliminary action by pre-training the encoder and decoder neural networks on the specific data type before actual compression operations. During this pre-training phase, the system learns optimal compression pathways and reconstruction methods specific to the data characteristics. When compression is needed, the pre-trained model can quickly compress and reconstruct data without requiring complex real-time computations, thus reducing processing time while maintaining compression effectiveness.
Solution Approach 2:
The patent replaces traditional mechanical compression algorithms with a neural network-based system. Instead of using deterministic compression rules and transformations, the system uses learned neural network mappings that can be evaluated efficiently during inference. This substitution allows the system to achieve comparable or better compression ratios with significantly reduced processing time once the model is trained, as the forward propagation through the neural network is computationally efficient.
3Productivity
If data is compressed to improve transmission efficiency, then bandwidth usage is reduced, but information fidelity may be compromised
Solution Approach 1:
The patent implements feedback through the decoder neural network that reconstructs the original data from the compressed representation. The reconstruction is compared with the original input data during training, and the difference (loss) is fed back to adjust the encoder and decoder parameters. This feedback mechanism ensures that the compressed representation retains sufficient information for accurate reconstruction, thus maintaining information fidelity while achieving efficient compression for transmission.
Solution Approach 2:
The patent applies partial action by compressing only the essential features and characteristics of the data while discarding redundant information. The neural network learns to identify and retain the most important data elements that contribute to information fidelity, compressing the data to a程度 that is sufficient for reconstruction purposes. This selective compression approach maintains fidelity for critical information while achieving high compression ratios for transmission efficiency.
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.