Federated Deep Learning with Homomorphic Compression for Low-Latency Privacy
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Solution Overview
Problem
Existing federated learning systems face challenges with communication efficiency, model performance on heterogeneous data, and ensuring end-to-end privacy guarantees, particularly in scenarios requiring low-latency processing and strong privacy preservation, such as healthcare diagnostics, financial fraud detection, or satellite telemetry systems.
Innovation Solution
A system and method for federated deep learning that integrates learning-based lossless data compression with privacy-preserving techniques, using homomorphic encryption and distributed learning principles to process encrypted data without decryption, supporting Transformer and Latent Transformer architectures, and implementing differential privacy and secure codebook management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If federated learning systems process data across distributed nodes, then collaborative model training is enabled, but communication overhead and latency increase
Solution Approach 1:
The patent extracts only the essential model updates (weight differences, gradients) from the full model parameters for transmission across distributed nodes. By sending only the differential changes rather than complete models, communication overhead is significantly reduced while maintaining the ability to perform collaborative training across multiple nodes.
Solution Approach 2:
The patent segments the model training process into discrete update iterations, where each node processes local data and transmits only incremental updates. This segmentation allows for asynchronous processing and reduces the time any single node must wait for communication, thereby reducing overall latency while enabling collaborative training.
2Productivity
If data is compressed using lossy compression, then communication efficiency improves, but data integrity and privacy are compromised
Solution Approach 1:
The patent creates compressed representations (copies) of model updates that capture the essential information needed for training while discarding redundant details. These compressed copies are sufficient for model convergence but reduce communication bandwidth requirements. The system maintains reliability by ensuring the compressed updates preserve the critical gradient information needed for accurate model training.
Solution Approach 2:
The patent changes the parameter representation of model updates by transforming full-precision floating-point values into lower-precision formats or quantized representations. This parameter transformation reduces communication overhead while maintaining sufficient accuracy for model training, achieving a balance between communication efficiency and data integrity.
3Reliability
If encryption is applied to preserve privacy, then data confidentiality is improved, but processing speed and computational efficiency decrease
Solution Approach 1:
The patent applies encryption and compression operations to model updates before they are generated and transmitted. By performing these privacy-preserving operations preliminarily on the update data rather than on raw inputs or during model inference, the system minimizes the impact on processing speed while maintaining confidentiality.
Solution Approach 2:
The patent extracts only the necessary model update information that requires privacy protection, rather than encrypting entire datasets or complete model parameters. By selectively applying encryption only to the differential updates that contain sensitive information, the system reduces computational overhead while maintaining data confidentiality.
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.