Federated latent transformer deep learning core
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 computer system comprising a hardware memory, wherein the computer system is configured to execute software instructions stored on nontransitory machine-readable storage media that, when executed by at least one processor, cause the computer system to:
orchestrate federated deep learning with a plurality of client devices by receiving a plurality of encrypted codewords from the plurality of client devices;
process the encrypted codewords using a deep learning core without decrypting the codewords; and
generate as output an encrypted codeword response to the input using the deep learning core;
wherein the deep learning core comprises a latent transformer core that was initially trained on encrypted codeword inputs to predict a plurality of probable future encrypted codewords that extend the input sequence.
2 . The system of claim 1 , wherein the deep learning core comprises a latent transformer architecture.
3 . The system of claim 2 , wherein each client device further comprises a variational autoencoder encoder that generates latent space vectors from the plurality of codewords.
4 . The system of claim 3 , wherein the latent transformer architecture processes the latent space vectors.
5 . The system of claim 1 , wherein the plurality of programming instructions further cause the computing device to:
aggregate encrypted model updates from the plurality of client devices;
update the deep learning core based on the aggregated encrypted model updates; and
facilitate federated learning by iteratively updating the deep learning core based on encrypted updates from the client devices.
6 . The system of claim 5 , 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.
7 . A method for federated deep learning using homomorphically-compressed and encrypted data, comprising the steps of:
orchestrating federated deep learning with a plurality of client devices by receiving a plurality of encrypted codewords from the plurality of client devices;
processing the encrypted codewords using a deep learning core without decrypting the codewords; and
generating as output an encrypted codeword response to the input using the deep learning core;
wherein the deep learning core comprises a latent transformer core that was initially trained on encrypted codeword inputs to predict a plurality of probable future encrypted codewords that extend the input sequence.
8 . The method of claim 7 , wherein the deep learning core comprises a latent transformer architecture.
9 . The method of claim 8 , wherein each client device further comprises a variational autoencoder encoder that generates latent space vectors from the plurality of codewords.
10 . The method of claim 9 , wherein the latent transformer architecture processes the latent space vectors.
11 . The method of claim 10 , further comprising the step of generating output vectors from processed latent space vectors using a variational autoencoder decoder.
12 . The method of claim 7 , further comprising the steps of:
aggregating encrypted model updates from the plurality of client devices;
updating the deep learning core based on the aggregated encrypted model updates; and
facilitating federated learning by iteratively updating the deep learning core based on encrypted updates from the client devices.
13 . The method of claim 12 , 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.