Distributed Ledger Worker Nodes Collective Learning
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Solution Overview
Problem
Conventional methods for collective learning in distributed computing systems face inefficiencies due to straggling worker nodes, staleness, and resource mismanagement, particularly in asynchronous training processes, leading to reduced system performance and stability.
Innovation Solution
A distributed computer control system utilizing a distributed ledger arrangement and smart contracts to coordinate collective learning among worker nodes, ensuring accountability, reliability, and efficient resource utilization through dynamic synchronization mode switching and reward mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If synchronous training process is used, then training coordination is improved, but system time efficiency deteriorates due to straggler nodes
Solution Approach 1:
The patent implements dynamic synchronization mode switching that allows the system to transition between synchronous and asynchronous training processes based on real-time system conditions. This enables the system to adapt its coordination mechanism dynamically, using synchronous mode when coordination is critical and asynchronous mode when time efficiency is prioritized, thereby resolving the contradiction between training coordination and system time efficiency.
2Productivity
If asynchronous training process is used, then system time efficiency is improved, but training reliability deteriorates due to staleness
Solution Approach 1:
The system dynamically adjusts the synchronization mode based on monitored performance metrics and system state. When training reliability is compromised due to staleness in asynchronous mode, the system can switch to synchronous mode to ensure proper coordination. This dynamic adaptation allows the system to maintain high productivity while preserving training reliability when necessary.
Solution Approach 2:
The patent incorporates feedback mechanisms that monitor training progress, node performance, and model staleness. This feedback information is used to determine when to switch between synchronous and asynchronous modes, ensuring that the system maintains optimal balance between time efficiency and training reliability based on actual system conditions.
3Reliability
If centralized parameter servers are used, then model aggregation coordination is improved, but system complexity and single point of failure risk increase
Solution Approach 1:
The patent extracts the coordination function from a centralized parameter server architecture and distributes it across multiple worker nodes through peer-to-peer communication mechanisms. This eliminates the single point of failure inherent in centralized systems while maintaining model aggregation coordination through distributed consensus protocols, thereby reducing system architecture complexity and improving reliability.
Solution Approach 2:
The system merges the roles of parameter server and worker nodes, allowing worker nodes to perform both training computations and model aggregation coordination. This consolidation eliminates the need for separate centralized parameter servers, reducing system complexity while maintaining coordinated model aggregation through the distributed worker node network.
4Adaptability or versatility
If worker nodes operate remotely in different geographical locations, then system scalability is improved, but training coordination efficiency deteriorates
Solution Approach 1:
The patent introduces intermediary synchronization mechanisms and communication protocols that facilitate efficient coordination between geographically distributed worker nodes. These intermediaries manage data synchronization, model aggregation, and coordination tasks, enabling remote nodes to collaborate effectively despite geographical separation, thereby maintaining training coordination efficiency while preserving system scalability.
Data Source
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AI summary
There is provided a distributed computer control system and a method of operation thereof. The distributed computer control system includes a plurality of worker nodes. The collective learning of the worker nodes occurs within the distributed computer control system. The operation of distributed computer control system is coordinated by employing a distributed ledger arrangement. The distributed computer control system comprises a monitoring arrangement to provide learning parameters to the worker nodes by way of executing a smart contract. Each of the worker nodes is operable to train a computing model based on the learning parameters and a set of training data. The monitoring arrangement provides a reward to at least one of the worker nodes for training the computing model. The reward is determined based on a number and frequency of intermediary computing models provided by a given worker node to a remainder of the worker nodes, and a quality of the intermediary computing models.