Federated large codeword model deep learning architecture with homomorphic compression and encryption
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.
1. A system for federated deep learning using homomorphically-compressed and encrypted data, comprising:
a computing device comprising at least a memory and a processor;
a plurality of programming instructions stored in the memory and operable on the processor, wherein the plurality of programming instructions, when operating on the processor, cause the computing device to:
receive encrypted codewords from a plurality of client devices, each client device having:
a local dataset; and
a compression network that converts the local dataset into a plurality of encrypted codewords;
process the encrypted codewords using a deep learning core without decrypting the codewords;
aggregate encrypted model updates from the plurality of client devices;
update the deep learning core based on the aggregated encrypted model updates;
train the deep learning core using the homomorphically encrypted codewords from the plurality of client devices; and
facilitate federated learning by iteratively updating the deep learning core based on encrypted updates from the client devices.
2. The system of claim 1 , wherein the deep learning core comprises a transformer-based machine learning architecture.
3. The system of claim 2 , wherein each client device further comprises a codebook generation subsystem that generates a codebook mapping sourceblocks to codewords.
4. The system of claim 3 , wherein the codeword allocator assigns codewords to sourceblocks based on the codebook.
5. The system of claim 2 , wherein the transformer-based machine learning architecture comprises:
an embedding layer;
a positional encoding layer;
a multi-head attention mechanism; and
a feed-forward network.
6. The system of claim 1 , wherein the deep learning core comprises a latent transformer architecture.
7. The system of claim 6 , wherein each client device further comprises a variational autoencoder encoder that generates latent space vectors from the plurality of codewords.
8. The system of claim 7 , wherein the latent transformer architecture processes the latent space vectors without using an embedding layer and a positional encoding layer.
9. The system of claim 8 , wherein the computing device further comprises a variational autoencoder decoder that generates output vectors from processed latent space vectors.
10. The system of claim 1 , wherein the plurality of programming instructions further cause the computing device to:
implement differential privacy by:
adding calibrated noise to the encrypted model updates before aggregation;
enforcing a privacy budget across multiple rounds of federated learning; and
dynamically adjusting the level of noise based on the privacy budget consumption;
thereby enhancing privacy guarantees for individual client datasets while maintaining model utility.
11. A method for federated deep learning using homomorphically-compressed and encrypted data, comprising the steps of:
receiving encrypted codewords from a plurality of client devices, each client device having:
a local dataset; and
a compression network that converts the local dataset into a plurality of encrypted codewords;
processing the encrypted codewords using a deep learning core without decrypting the codewords;
aggregating encrypted model updates from the plurality of client devices;
updating the deep learning core based on the aggregated encrypted model updates;
training the deep learning core using the homomorphically encrypted codewords from the plurality of client devices; and
facilitating federated learning by iteratively updating the deep learning core based on encrypted updates from the client devices.
12. The method of claim 11 , wherein the deep learning core comprises a transformer-based machine learning architecture.
13. The method of claim 12 , wherein each client device further comprises a codebook generation subsystem that generates a codebook mapping sourceblocks to codewords.
14. The method of claim 13 , wherein the codeword allocator assigns codewords to sourceblocks based on the codebook.
15. The method of claim 12 , wherein the transformer-based machine learning architecture comprises:
an embedding layer;
a positional encoding layer;
a multi-head attention mechanism; and
a feed-forward network.
16. The method of claim 11 , wherein the deep learning core comprises a latent transformer architecture.
17. The method of claim 16 , wherein each client device further comprises a variational autoencoder encoder that generates latent space vectors from the plurality of codewords.
18. The method of claim 17 , wherein the latent transformer architecture processes the latent space vectors without using an embedding layer and a positional encoding layer.
19. The method of claim 18 , further comprising the step of generating output vectors from processed latent space vectors using a variational autoencoder decoder.
20. The method of claim 11 , further comprising the steps of:
implementing differential privacy by:
adding calibrated noise to the encrypted model updates before aggregation;
enforcing a privacy budget across multiple rounds of federated learning; and
dynamically adjusting the level of noise based on the privacy budget consumption;
thereby enhancing privacy guarantees for individual client datasets while maintaining model utility.