Decentralized Statistical Set Averaging for ML Model Training
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Solution Overview
Problem
Decentralized distributed training of machine learning models faces challenges in converging to consensus efficiently due to high communication costs and processing power limitations, especially when dealing with large-scale statistical sets that are non-centrally located, leading to slow convergence rates and memory-intensive processes.
Innovation Solution
A system and method for updating statistical sets through randomized distributed averaging, where nodes communicate according to a randomization pattern, selecting subsets to reduce communication costs and increase convergence rates, allowing for faster updating without feedback, and potentially introducing noise to find more robust local optima.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If decentralized distributed training is performed with large-scale statistical sets, then model training can be distributed across multiple nodes, but communication costs increase and convergence rate decreases
Solution Approach 1:
The patent segments the statistical sets into multiple partitions distributed across different nodes. Each node maintains local statistical set partitions and performs local averaging operations, reducing the need for full statistical set communication between nodes while maintaining distributed training capability
Solution Approach 2:
The patent implements partial averaging where nodes selectively average only certain partitions of statistical sets rather than complete statistical sets. This partial action reduces communication overhead and computational burden while still achieving model convergence through iterative local updates
2Reliability
If complete statistical sets are averaged across all nodes, then consensus is achieved, but memory usage and processing requirements increase significantly
Solution Approach 1:
The statistical sets are divided into multiple partitions that are distributed across nodes. Each node stores only local partitions rather than complete statistical sets, significantly reducing memory requirements while maintaining the ability to achieve consensus through iterative averaging of available partitions
Solution Approach 2:
Nodes perform averaging operations on partial statistical set partitions rather than complete sets. This partial averaging reduces the quantity of data that must be stored and processed at each node while still progressing toward consensus through multiple iterations
3Productivity
If statistical sets are averaged frequently, then convergence rate improves, but communication costs and processing overhead increase
Solution Approach 1:
The patent implements periodic partial averaging where nodes average only certain statistical set partitions at selected intervals rather than continuously. This reduces communication frequency and associated costs while maintaining adequate convergence through strategic partial updates
Data Source
AI summary
Systems, computer-implemented methods, and computer program products to facilitate updating, such as averaging and/or training, of one or more statistical sets are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can include a computing component that averages a statistical set, provided by the system, with an additional statistical set, that is compatible with the statistical set, to compute an averaged statistical set, where the additional statistical set is obtained from a selected additional system of a plurality of additional systems. The computer executable components also can include a selecting component that selects the selected additional system according to a randomization pattern.