US-12627315-B2 - Latent transformer architecture with attention mechanisms and expert systems for federated deep learning with homomorphic encryption

Abstract

A latent transformer architecture with latent attention mechanisms and expert processing systems for federated deep learning. The latent transformer operates entirely within latent space, eliminating traditional embedding and positional encoding layers while maintaining full attention capabilities. Input data is compressed into latent vectors via variational autoencoder encoding, then processed by a latent attention module that computes query, key, and value matrices directly from latent representations. The architecture incorporates expert processing systems including gated latent expert networks for sparse computation and latent mixture of experts for collaborative processing. In the gated approach, a routing network selectively activates specialized expert modules based on latent vector characteristics. The mixture approach enables all experts to contribute through weighted combination, facilitating distributed computation and enhanced model expressiveness.

Inventors

• Brian Galvin

Assignees

• AtomBeam Technologies Inc.

Dates

Publication Date

20260512

Application Date

20251006

Claims (17)

1. 1 . A system for latent space processing with attention mechanisms, comprising: a computing device comprising a processor and memory storing instructions that, when executed, cause the computing device to: receive input data; generate latent space representations of the input data using an encoding mechanism; process the latent space representations using a latent processing subsystem that operates directly on the latent space representations without token embedding layers or positional encoding layers; route the processed latent space representations through an expert processing system comprising a plurality of expert modules, wherein different expert modules are configured to provide specialized processing capabilities; and generate output data from the expert-processed latent space representations using a decoding mechanism; wherein the expert processing system operates in a gated configuration where a gating module selectively activates a subset of the plurality of expert modules based on characteristics of the latent space representations; and wherein the gating module employs selection strategies selected from the group consisting of: top-k selection, entropy-based scoring, threshold-based routing, and learned assignment functions.
2. 2 . The system of claim 1 , wherein the latent processing subsystem comprises a transformer-based architecture that computes attention mechanisms directly on the latent space representations by generating query, key, and value matrices from the latent space representations.
3. 3 . The system of claim 1 , wherein the expert processing system incorporates both gated and mixture configurations that can be dynamically selected based on computational constraints or data characteristics.
4. 4 . The system of claim 1 , wherein the expert processing system operates in a mixture configuration where all expert modules process the latent space representations and outputs are combined using weighted aggregation computed by a gating network.
5. 5 . The system of claim 1 , wherein the encoding mechanism comprises a variational autoencoder encoder and the decoding mechanism comprises a variational autoencoder decoder.
6. 6 . The system of claim 1 , wherein the system further comprises a codeword allocator that converts the input data into codewords using semantic splitting and codebook mapping before generating the latent space representations.
7. 7 . The system of claim 1 , wherein the latent space representations are homomorphically encrypted, enabling processing without decryption for federated learning across multiple client devices.
8. 8 . The system of claim 1 , wherein the input data comprises multiple data modalities, and the output data is generated in a same or different modality than the input data.
9. 9 . A method for latent space processing with attention mechanisms, comprising the steps of: receiving input data; generating latent space representations of the input data using an encoding mechanism; processing the latent space representations using a latent processing subsystem that operates directly on the latent space representations without token embedding layers or positional encoding layers; routing the processed latent space representations through an expert processing system comprising a plurality of expert modules, wherein different expert modules are configured to provide specialized processing capabilities; converting the input data into codewords using semantic splitting and codebook mapping before generating the latent space representations; and generating output data from the expert-processed latent space representations using a decoding mechanism.
10. 10 . The method of claim 9 , wherein the latent processing subsystem comprises a transformer-based architecture that computes attention mechanisms directly on the latent space representations by generating query, key, and value matrices from the latent space representations.
11. 11 . The method of claim 9 , wherein the expert processing system operates in a gated configuration where a gating module selectively activates a subset of the plurality of expert modules based on characteristics of the latent space representations.
12. 12 . The method of claim 11 , wherein the gating module employs selection strategies selected from the group consisting of: top-k selection, entropy-based scoring, threshold-based routing, and learned assignment functions.
13. 13 . The method of claim 9 , wherein the expert processing system incorporates both gated and mixture configurations that are dynamically selected based on computational constraints or data characteristics.
14. 14 . The method of claim 9 , wherein the expert processing system operates in a mixture configuration where all expert modules process the latent space representations and outputs are combined using weighted aggregation computed by a gating network.
15. 15 . The method of claim 9 , wherein the encoding mechanism comprises a variational autoencoder encoder and the decoding mechanism comprises a variational autoencoder decoder.
16. 16 . The method of claim 9 , wherein the input data comprises multiple data modalities, and the output data is generated in a same or different modality than the input data.
17. 17 . The method of claim 9 , wherein the latent space representations are homomorphically encrypted, enabling processing without decryption for federated learning across multiple client devices.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS Priority is claimed in the application data sheet to the following patents or patent applications, each of which is expressly incorporated herein by reference in its entirety: Ser. No. 19/026,345Ser. No. 18/919,394Ser. No. 18/737,906Ser. No. 18/736,498Ser. No. 63/651,359 BACKGROUND OF THE INVENTION Field of the Art The present invention is in the field of data compression, and more particularly is directed to the problem of efficiently compressing large sets of data without losing information. Discussion of the State of the Art Modern deep learning architectures, particularly transformer-based models, have demonstrated remarkable capabilities but face significant limitations in distributed learning, data privacy, and computational efficiency. Traditional transformer architectures rely heavily on embedding layers and positional encoding mechanisms that introduce substantial computational overhead and memory requirements, particularly in resource-constrained or distributed environments. Federated learning enables collaborative machine learning without centralizing sensitive data, but existing systems face critical privacy challenges. Current approaches require sharing model parameters or gradient information, which can leak sensitive information through gradient inversion attacks, membership inference attacks, and model extraction techniques. These vulnerabilities limit practical deployment in sensitive domains such as healthcare and finance. While homomorphic encryption provides theoretical solutions for privacy-preserving computation, practical implementation in machine learning faces significant challenges. Existing approaches require specialized encryption schemes, introduce substantial computational overhead, and limit the types of operations that can be performed efficiently. Integration with complex deep learning architectures remains technically challenging. Expert systems and mixture of experts architectures attempt to address scalability challenges but face their own limitations. Traditional mixture of experts approaches require dense computation across all expert modules, while gated expert systems struggle with effective routing decisions and load balancing. Current architectures also face challenges in distributed environments with varying computational capabilities. Existing data compression techniques in machine learning focus primarily on storage or bandwidth reduction rather than enabling privacy-preserving computation. Many compression algorithms introduce information loss or are incompatible with homomorphic encryption operations, limiting their utility in privacy-preserving scenarios. What is needed is a unified system that addresses these interconnected challenges through efficient latent space processing, privacy-preserving computation, and collaborative learning capabilities. Such a system should eliminate the computational overhead of traditional embedding layers while maintaining attention mechanism capabilities, enable effective federated learning with strong privacy guarantees, and incorporate flexible expert processing architectures suitable for diverse computational constraints and data modalities. SUMMARY OF THE INVENTION Accordingly, the inventor has conceived and reduced to practice, a latent transformer system with latent attention mechanisms and expert processing systems for federated deep learning is disclosed. The system operates entirely within latent space, eliminating traditional embedding and positional encoding layers while maintaining full attention capabilities. Input data is compressed into latent vectors via variational autoencoder encoding, then processed by a latent attention module that computes query, key, and value matrices directly from latent representations. The architecture incorporates expert processing systems including gated latent expert networks for sparse computation and latent mixture of experts for collaborative processing. In the gated approach, a routing network selectively activates specialized expert modules based on latent vector characteristics. The mixture approach enables all experts to contribute through weighted combination, facilitating distributed computation and enhanced model expressiveness. According to a preferred embodiment, a system for latent space processing with attention mechanisms, comprising: a computing device comprising a processor and memory storing instructions that, when executed, cause the computing device to: receive input data; generate latent space representations of the input data using an encoding mechanism; process the latent space representations using a latent processing subsystem that operates directly on the latent space representations without token embedding layers or positional encoding layers; route the processed latent space representations through an expert processing system comprising a plurality of expert modules, wherein different expert modules are configured