Pruned Relational Graph for Distributed Wireless Learning
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Solution Overview
Problem
Existing machine learning techniques for wireless access networks face challenges in data sharing due to privacy issues and large state-spaces, limiting the effectiveness of distributed machine learning applications.
Innovation Solution
A method for distributed machine learning in wireless access networks involves defining neighbor relation edges with associated key performance indicators (KPIs), forming a pruned relational graph to reduce complexity, and using reinforcement learning agents to optimize network parameters, allowing for efficient and accurate distributed machine learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If distributed machine learning is applied in wireless access networks using all neighbor relations, then model accuracy is improved, but computational load and training time increase significantly
Solution Approach 1:
The patent extracts and removes irrelevant or less important neighbor relation edges from the graph, keeping only the most relevant connections for machine learning training. This pruning approach reduces the state-space complexity while preserving the essential relationships needed for accurate model training, directly addressing the contradiction between accuracy and computational load.
Solution Approach 2:
The patent applies different treatment to different parts of the network graph by selectively pruning edges based on local characteristics and relevance metrics. Each node's neighbor relations are evaluated individually, and edges are retained or removed based on their specific contribution to learning, rather than applying a uniform approach across the entire network.
2Measurement precision
If distributed machine learning is applied in wireless access networks using all neighbor relations, then model accuracy is improved, but training convergence time increases
Solution Approach 1:
By extracting and removing redundant neighbor relation edges from the training graph, the patent reduces the overall state-space that the machine learning model must process. This leads to faster training convergence while maintaining the essential relationships needed for accurate predictions, directly addressing the time-accuracy tradeoff.
Solution Approach 2:
The patent applies partial action by using only a subset of available neighbor relations rather than all possible connections. This selective approach provides sufficient information for accurate model training without the excessive computational burden of processing every possible neighbor relation, enabling faster convergence.
3Productivity
If data is freely shared among network nodes for machine learning, then learning effectiveness is improved, but privacy issues and security risks worsen
Solution Approach 1:
The patent segments the network data into local node-specific data and shared graph structure information. Each node retains its private data locally while sharing only the topological structure (pruned graph) with neighbors, enabling collaborative learning without exposing sensitive private information, thus resolving the privacy-productivity contradiction.
Solution Approach 2:
The pruned graph structure acts as an intermediary that enables information exchange between nodes without directly sharing sensitive data. The graph topology serves as a mediator that captures essential relationships while filtering out private information, allowing effective distributed learning while maintaining privacy.
Data Source
AI summary
A computer-implemented method for distributed machine learning, performed in a wireless access network including a plurality of nodes. The method includes: defining a set of neighbor relation edges which connect at least some of the nodes of the wireless access network, where each neighbor relation edge is associated with at least one neighbor relation (KPI), selecting a subset of the neighbor relation edges for each node, wherein the selected subset of neighbor relation edges is associated with one or more neighbor relation KPI that meets a pre-determined acceptance criterion, forming a relational graph for each node based on the respective selected subset of neighbor relation edges for the node, and performing distributed machine learning over the nodes in the wireless access network based on the formed relational graph for each node.