Fusion Server Agent Clustering for Distributed ML
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Solution Overview
Problem
Distributed machine learning systems face challenges in allocating complex learning processes across multiple independent agents due to limited communication resources and data privacy issues, which hinder effective collaboration and security.
Innovation Solution
A fusion server determines correlation relationships across agents based on auxiliary information, clusters them into communities, and selectively chooses participating agents to enhance distributed machine learning performance, ensuring balanced datasets, reduced communication overhead, and improved security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple independent agents are used in distributed machine learning, then the system can handle data privacy issues and distribute computational load, but communication resources are limited and collaboration effectiveness deteriorates
Solution Approach 1:
The patent segments agents into different types (data collectors, label generators, model trainers) based on their functional roles and correlation relationships. This segmentation allows the system to optimize communication by routing information through appropriate agent types, reducing unnecessary communication overhead while maintaining distributed computational capability.
Solution Approach 2:
The patent introduces a fusion server as an intermediary that coordinates communication between agents. The fusion server aggregates information from multiple agents, performs fusion operations, and distributes results back to agents, thereby reducing direct peer-to-peer communication requirements and improving overall communication efficiency.
2Reliability
If agents are treated as independent entities, then data privacy is maintained, but collaboration and learning performance deteriorate due to lack of coordination
Solution Approach 1:
The patent segments the system into independent agents that each maintain their own data locally, preserving data privacy. Meanwhile, these segmented agents are organized into correlated groups that collaborate through defined interfaces, allowing improved learning performance without compromising individual data privacy.
Solution Approach 2:
The patent creates multi-functional agents that can perform multiple roles (data collection, labeling, training) while maintaining their independence. This universality allows agents to contribute to multiple learning tasks simultaneously, improving overall system performance while each agent continues to protect its own data privacy.
3Quantity of substance
If all agents participate equally in distributed machine learning, then comprehensive data coverage is achieved, but communication overhead and computational waste increase
Solution Approach 1:
The patent segments agents into specialized groups based on their correlation relationships and functional capabilities. This segmentation allows the system to achieve comprehensive data coverage by selecting from diverse agent groups while minimizing communication overhead by limiting participation to only those agents relevant to each specific learning task.
Solution Approach 2:
The patent implements partial participation where not all agents are involved in every learning iteration. Instead, subsets of agents are selected based on their correlation relationships and task relevance, achieving sufficient data coverage with reduced communication overhead and computational resource consumption.
4Productivity
If agents with high correlation are grouped together, then collaboration efficiency improves, but diversity of perspectives and data variability deteriorates
Solution Approach 1:
The patent segments agents into multiple correlated groups, where each segment collaborates efficiently internally. Meanwhile, the system maintains multiple such segments with different correlation patterns, preserving data diversity by drawing from multiple segmented groups for comprehensive learning tasks.
Solution Approach 2:
The patent creates composite agent groups that combine agents with different correlation characteristics. These composite groups maintain internal collaboration efficiency while introducing diversity through the inclusion of agents with varying correlation relationships, effectively balancing both requirements.
Data Source
AI summary
A computer-implemented method, a computer program product, and a computer system for enhanced distributed machine learning. A fusion server in a distributed machine learning system determines correlation relationships across agents in the distributed machine learning system, based on auxiliary information. The fusion server clusters the agents to form one or more communities, based on the correlation relationships. The fusion server selects, from the one or more communities, participating agents that participate in the enhanced distributed machine learning.