Distributed Learning Parameter Aggregation via Sign Extraction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Distributed learning systems face bottlenecks in aggregating learning results from multiple local devices, leading to increased learning time and decreased performance due to the need for central servers to collect and process local parameters, which can result in a bottleneck situation and slow convergence.
Innovation Solution
A method and apparatus for determining the number of local parameters to be aggregated, where the system receives and updates local parameters based on sign differences between consecutive global parameters, optimizing the number of parameters used and reducing network hops, allowing for adaptive adjustment in the presence of stragglers and maintaining learning speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all local parameters from multiple distributed learning processing apparatuses are aggregated at the central server, then learning completeness is improved, but network bottleneck and learning time increase
Solution Approach 1:
The patent extracts only the essential information (sign of global parameter) from local parameters instead of aggregating all local parameters. The central server determines the sign of the global parameter by comparing signs of local parameters received from distributed learning processing apparatuses, thereby avoiding the bottleneck of transmitting and processing large volumes of parameter data while still ensuring learning completeness.
Solution Approach 2:
The patent introduces an intermediary mechanism where the central server acts as a coordinator that receives local parameters, determines the sign of the global parameter, and then distributes this sign information back to all learning processing apparatuses. This intermediary approach avoids direct peer-to-peer aggregation bottlenecks and reduces network traffic.
2Measurement precision
If local parameters are transmitted from multiple distributed learning processing apparatuses to the central server, then learning accuracy is improved, but network traffic increases
Solution Approach 1:
The patent extracts only the critical feature (sign value) from local parameters for aggregation purposes. Instead of transmitting and processing complete local parameter values across the network, the system determines the global parameter sign by comparing signs of received local parameters, thereby maintaining learning accuracy while minimizing network traffic to the essential information needed.
Solution Approach 2:
The patent inverts the traditional approach by not aggregating local parameters directly to compute global parameters. Instead, the central server determines the sign of the global parameter by comparing signs of local parameters, and then distributes this determined sign back to all apparatuses for their local updates. This inversion reduces network traffic while preserving learning accuracy.
3Adaptability or versatility
If the central server aggregates learning results from multiple local devices, then distributed learning capability is improved, but bottleneck situation occurs
Solution Approach 1:
The patent extracts only the sign information from local parameters for aggregation, rather than processing complete parameter sets. This extraction approach maintains distributed learning capability by allowing multiple apparatuses to contribute to the global model, while significantly improving aggregation speed by reducing the data volume that needs to be processed at the central server.
Solution Approach 2:
The patent applies partial action by determining the global parameter sign using only a subset of information (signs of local parameters) rather than processing all local parameter values. This partial approach maintains the distributed learning capability while improving aggregation speed by performing only the essential comparison operation needed to determine the sign.
Data Source
AI summary
Provided are a learning processing system, and an apparatus and method for determining a number of local parameters. A method of determining a number of local parameters may include receiving a number of local parameters less than or equal to a number of local parameters to be aggregated from at least one distributed learning processing apparatus; acquiring a T-th global parameter using the number of local parameters less than or equal to the number of local parameters to be aggregated; and updating or maintaining the number of local parameters to be aggregated depending on whether signs are different between a (T−1)-th global parameter and the T-th global parameter.