Gradient Encoding for Distributed Learning Bandwidth
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Solution Overview
Problem
Training deep neural networks in distributed learning systems faces challenges due to high communication rates and limited bandwidth, especially in wireless networks, which hinders efficient exchange of gradient information.
Innovation Solution
The method encodes gradient information by exploiting correlations across computing nodes, reducing redundancy and compressing data before exchange, using protocols like ring-allreduce and parameter-server communication protocols to aggregate and update learning models efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If gradient information is exchanged across computing nodes in a distributed learning system, then the learning model can be trained effectively, but the communication bandwidth requirements become excessively high
Solution Approach 1:
The patent combines gradient information from multiple computing nodes and exploits correlations across these gradients. By merging the gradient data and identifying common patterns, the system transmits only the combined correlated information rather than individual gradients from each node, thereby reducing total communication bandwidth while preserving training effectiveness
Solution Approach 2:
The patent transforms the gradient information by encoding it in a correlated representation. This parameter transformation converts full-gradient transmission into a compressed correlated-form transmission, changing the state of the data from redundant individual gradients to efficient combined representation, thus reducing communication requirements
2Quantity of substance
If gradient information is compressed and encoded before exchange, then communication bandwidth requirements are reduced, but the complexity of the encoding and decoding process increases
Solution Approach 1:
The patent performs encoding of gradient information in advance before the actual exchange occurs. By pre-computing the correlated representation and preparing the encoded data structure beforehand, the system reduces the complexity during the critical exchange phase, making the overall process more manageable despite the added encoding step
Data Source
AI summary
A computer implemented method for training a learning model by a distributed learning system includes computing nodes. The computing nodes respectively implement the learning model and deriving a gradient information for updating the learning model based on training data. The method involves: encoding, by the computing nodes, the gradient information by exploiting a correlation across the gradient information from the respective computing nodes; exchanging, by the computing nodes, the encoded gradient information within the distributed learning system; determining an aggregate gradient information based on the encoded gradient information from the computing nodes; and updating the learning model of the computing nodes with the aggregate gradient information, thereby training the learning model.