Latent Transformer Architecture for Federated Deep Learning
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Solution Overview
Problem
Modern deep learning architectures face challenges in distributed learning, data privacy, and computational efficiency, particularly in transformer-based models, which require significant computational overhead and memory, and existing federated learning systems lack effective privacy-preserving computation and scalable architectures.
Innovation Solution
A latent transformer system operates in latent space, eliminating embedding and positional encoding layers, using variational autoencoders for data compression and incorporating gated latent expert networks and latent mixture of experts for flexible processing, enabling privacy-preserving computation and collaborative learning across diverse computational environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional transformer architectures with embedding layers and positional encoding are used, then attention capabilities are maintained, but computational overhead and memory requirements increase substantially
Solution Approach 1:
The patent extracts and removes the embedding layer and positional encoding mechanisms from the traditional transformer architecture, operating directly on latent space representations. This elimination reduces computational overhead and memory requirements while preserving the core attention mechanism's ability to process relationships between data elements.
Solution Approach 2:
The architecture segments the processing pipeline into distinct components: latent space encoding, attention-based processing, and expert module routing. This segmentation allows each component to operate independently and efficiently, reducing overall system complexity while maintaining functional capabilities.
2Productivity
If federated learning systems share model parameters or gradient information, then collaborative learning is enabled, but privacy is compromised through gradient inversion attacks, membership inference attacks, and model extraction techniques
Solution Approach 1:
The patent introduces latent space representations as an intermediary between the federated learning participants. Instead of sharing raw model parameters or gradients, systems exchange and process compressed latent representations that preserve collaborative learning benefits while obscuring sensitive information through the transformation process.
Solution Approach 2:
The system transforms model parameters into latent space representations through encoding, changing their form and properties. This parameter transformation maintains the functional capabilities needed for collaborative learning while making the parameters resistant to traditional privacy attacks that target raw parameter spaces.
3Object-affected harmful factors
If homomorphic encryption is implemented for privacy-preserving computation, then data privacy is protected, but computational overhead increases and operation types are limited
Solution Approach 1:
Instead of applying homomorphic encryption to all computations, the patent applies encryption selectively to specific operations and data flows within the architecture. This partial application maintains privacy protection for critical operations while reducing overall computational overhead and allowing unencrypted processing where privacy risks are lower.
4Reliability
If mixture of experts architectures use dense computation across all expert modules, then model expressiveness is enhanced, but computational efficiency decreases in resource-constrained environments
Solution Approach 1:
The patent implements dynamic routing mechanisms that adaptively activate expert modules based on the characteristics of the input latent representations. This dynamic approach allows the system to use multiple experts when needed for high expressiveness while switching to fewer active experts in resource-constrained scenarios, optimizing the trade-off between model capability and computational efficiency.
Data Source
AI summary
A latent transformer architecture 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.