Compressed Gradient Transmission in Distributed AI Systems
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Solution Overview
Problem
The high communication cost associated with transmitting and receiving parameters in distributed artificial intelligence systems, particularly in federated learning, where the cost is proportional to the size of the global model and the number of mobile devices connected, poses a significant challenge.
Innovation Solution
A method is proposed to compress gradient information of artificial intelligence models into a smaller amount of data using block sparsification and digital encoding, allowing for efficient transmission and reconstruction at a remote device, thereby reducing communication overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If gradient information is transmitted in full precision, then model training accuracy is maintained, but communication cost increases significantly
Solution Approach 1:
The gradient vector is divided into multiple blocks, and each block is processed independently through sparsification. This segmentation allows the system to reduce communication overhead by selectively transmitting only significant components while maintaining overall gradient accuracy for model training convergence.
Solution Approach 2:
The patent extracts and transmits only the significant components of gradient information (non-zero elements above a threshold) while discarding or locally retaining less important components. This extraction approach reduces the quantity of transmitted data while preserving the essential information needed for effective model updates.
2Productivity
If communication data is compressed, then transmission efficiency improves, but reconstruction error increases
Solution Approach 1:
The system performs preliminary sparsification and quantization on gradient blocks before transmission, preparing them in an optimized format. This preliminary action includes calculating thresholds, identifying significant elements, and applying quantization schemes that balance compression ratio with reconstruction fidelity, ensuring efficient transmission without excessive error.
Solution Approach 2:
The patent dynamically adjusts compression parameters such as sparsification thresholds, quantization bit-depth, and block sizes based on gradient characteristics and communication constraints. By changing these parameters adaptively, the system optimizes the trade-off between compression efficiency and reconstruction accuracy for different training scenarios.
Data Source
AI summary
A learning method of distributed artificial intelligence is disclosed. The method is comprising: updating, by the local device, parameters of a corresponding local model by training the local model based on a corresponding local dataset; generating, by the local device, compressed gradient information by compressing gradients of the parameter of the local model; transmitting, by the local device, the compressed gradient information to a parameter server; reconstructing, by a parameter server, gradients of the parameters of the local model based on the compressed gradient information received from the local device; and updating, by the parameter server, parameters of a global model based on the reconstructed gradients of the parameters of the local model.