Neural Network Parameter Update Tree for Low-Data Model Synchronization
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Solution Overview
Problem
Current neural network training techniques require transmitting all parameters, which is inefficient, especially in distributed learning scenarios, where only updates are necessary, leading to increased computational and communication costs.
Innovation Solution
The use of a decoder and encoder system that represents neural network parameters using a parameter update tree, where only differential information and a parent node identifier are transmitted, allowing for efficient reconstruction of updated parameters through recursive calculation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all neural network parameters are transmitted for training, then the neural network can be trained or updated, but the communication cost and data transmission volume increase significantly
Solution Approach 1:
The patent extracts only the differential information (changes) from the full parameter set and transmits only these differences. Instead of sending complete parameter tensors, the system identifies and transmits only the modified parameters along with their update values, significantly reducing transmission volume while preserving training capability.
Solution Approach 2:
The parameter update process is segmented into: (1) identifying which parameters have changed, (2) encoding only those changes with parent node identifiers, and (3) transmitting the segmented differential data. This segmentation allows the system to avoid transmitting unchanged parameters, reducing overall data volume.
2Quantity of substance
If differential information is used to represent parameter updates, then the data transmission volume is reduced, but the complexity of encoding and decoding increases
Solution Approach 1:
The system performs preliminary encoding by establishing a parameter update tree structure before transmission. Parent node identifiers are pre-assigned to represent specific parameter groups or tensors, allowing the decoder to efficiently reconstruct full parameters by combining these identifiers with received differential data, reducing decoding complexity.
Solution Approach 2:
The patent introduces an intermediary parameter update tree structure that mediates between the differential updates and the full parameter representation. This tree structure organizes parent node identifiers and their corresponding parameter groups, simplifying the mapping process during encoding and decoding operations.
3Productivity
If parent node identifiers are used to reference parameter groups, then the transmission efficiency is improved, but the memory requirements for storing the parameter update tree increase
Solution Approach 1:
The parameter update tree employs a nested structure where parent nodes contain references to child nodes, and each node stores only essential information (identifier and differential data). This nesting allows hierarchical organization of parameters where common parent parameters are stored once and referenced multiple times, reducing overall storage requirements compared to storing complete parameter copies at each level.
Data Source
AI summary
Disclosed is a decoder for decoding parameters of a neural network, configured to obtain a plurality of neural network parameters of the neural network on the basis of an encoded bitstream, to obtain, e.g. to receive; e.g. to extract from an encoded bitstream, a node information describing a node of a parameter update tree, wherein the node information has a parent node identifier, which is, for example, a unique parent node identifier, for example an integer number, a string, and/or a cryptographic hash, and wherein the node information has a parameter update information, e.g. one or more update instructions, for example a difference signal between initial neural network parameters and a newer version thereof, e.g. corresponding to a child node of the update tree, and to derive one or more neural network parameters using parameter information of a parent node identified by the parent node identifier and using the parameter update information.