Distributed Ledger Coordination for Collective Learning Stragglers
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Solution Overview
Problem
Conventional methods for collective learning of computing models face inefficiencies due to stragglers and staleness in training processes, particularly in asynchronous training, and require centralized authorities or network bandwidth optimization, which are not scalable for remote worker nodes.
Innovation Solution
A distributed computer system utilizing a distributed ledger arrangement and smart contracts to coordinate and manage collective learning among worker nodes, providing learning parameters and rewards based on the number, frequency, and quality of intermediary computing models, ensuring accountability and efficient resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If synchronous training process is used, then training coordination is improved, but time efficiency deteriorates due to worker nodes waiting for stragglers
Solution Approach 1:
The system dynamically switches between synchronous and asynchronous training modes based on real-time system state and performance metrics. The monitoring arrangement adjusts the training process from rigid synchronous coordination to flexible asynchronous execution, optimizing both reliability and time efficiency by adapting to actual worker node performance conditions.
2Loss of time
If asynchronous training process is used, then time efficiency is improved, but training coordination deteriorates due to staleness of intermediary models
Solution Approach 1:
The monitoring arrangement continuously monitors training progress, model quality, and worker node performance, providing feedback to dynamically adjust the training process. This feedback mechanism ensures that asynchronous training maintains coordination reliability by detecting and correcting staleness issues while preserving time efficiency benefits.
Solution Approach 2:
The system employs dynamic mode switching between synchronous and asynchronous training based on monitored performance metrics. When staleness is detected, the system can transition to synchronous mode or adjust asynchronous parameters, maintaining training coordination while preserving time efficiency where possible.
3Reliability
If centralized authority with parameter servers is used, then training coordination is improved, but system complexity and scalability deteriorate
Solution Approach 1:
The invention extracts the coordination function from a centralized parameter server architecture and distributes it to a monitoring arrangement that operates autonomously among worker nodes. This eliminates the need for complex centralized infrastructure while maintaining training coordination through decentralized monitoring and dynamic mode switching.
Solution Approach 2:
Worker nodes equip themselves with monitoring capabilities and autonomously participate in the training coordination process. The monitoring arrangement is distributed across the system rather than centralized, allowing nodes to self-manage their training processes while maintaining overall system coordination, thereby reducing system complexity.
4Ease of operation
If conventional methods are used, then training process is simplified, but adaptability to remote worker nodes and asynchronous operation deteriorates
Solution Approach 1:
The monitoring arrangement implements a universal training coordination mechanism that functions effectively across diverse worker node configurations, including remote nodes with varying capabilities. The dynamic mode switching and decentralized architecture provide adaptability to different network conditions and node performances while maintaining operational simplicity through automated monitoring and adjustment.
Data Source
AI summary
Disclosed is a distributed computer system and a method of operation thereof. The distributed computer system includes a plurality of worker nodes. The collective learning of the worker nodes is managed within the distributed computer system. The operation of distributed computer system is coordinated by employing a distributed ledger arrangement. The distributed computer system comprises a monitoring arrangement to provide learning parameters to the worker nodes by way of executing 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.