Federated Learning Quantization Selection for Heterogeneous Edge Nodes

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Solution Overview

Problem

Existing federated learning processes face issues with impaired edge node selection, leading to network traffic inefficiencies, delayed training cycles, and low diversification due to random selection of edge nodes, particularly those with resource constraints.

Innovation Solution

Intelligently select edge nodes based on resource availability and similarity clustering to ensure diverse and capable devices perform quantization, using statistics like processing time, data size, and network bandwidth to build clusters, thereby optimizing the quantization process.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If random selection of edge nodes is used for quantization assessment, then selection simplicity is improved, but node capability adequacy deteriorates

Engineering Contradiction:

Improveselection simplicityVSAvoidnode capability adequacy

Core Design Contradiction:

Ease of operationVSReliability

Solution Approach 1:

The patent changes the selection parameters from random selection to selection based on specific device parameters including available memory, processing power, and network bandwidth. The central node evaluates these parameters to identify edge nodes capable of performing quantization assessment, thereby resolving the contradiction between selection simplicity and node capability adequacy.

Inventive Principle:

Principle #35Parameter changes

2Adaptability or versatility

If resource-constrained edge nodes are selected for quantization assessment, then device diversity is improved, but processing speed deteriorates

Engineering Contradiction:

Improvedevice diversityVSAvoidprocessing speed

Core Design Contradiction:

Adaptability or versatilityVSSpeed

Solution Approach 1:

The patent applies local quality by selecting edge nodes with specific local characteristics - specifically nodes with adequate resources for quantization assessment. Instead of uniformly selecting from all nodes or focusing only on high-resource nodes, the system identifies and selects nodes with the appropriate local quality (sufficient but not excessive resources) to perform the assessment function effectively.

Inventive Principle:

Principle #3Local quality

3Adaptability or versatility

If impaired edge nodes are selected for quantization assessment, then selection randomness is improved, but federated learning cycle time deteriorates

Engineering Contradiction:

Improveselection randomnessVSAvoidfederated learning cycle time

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The patent implements preliminary action by having the central node evaluate edge node capabilities before selecting nodes for quantization assessment. The central node proactively identifies nodes with adequate resources and prepares the selection in advance, preventing the selection of impaired nodes that would cause delays. This preliminary capability assessment ensures that selected nodes can complete the assessment without stalling the federated learning cycle.

Inventive Principle:

Principle #10Preliminary action

4Power

If edge nodes with adequate resources are selected for quantization assessment, then processing capability is improved, but selection complexity deteriorates

Engineering Contradiction:

Improveprocessing capabilityVSAvoidselection complexity

Core Design Contradiction:

PowerVSDevice complexity

Solution Approach 1:

The patent implements feedback by having the central node evaluate edge node capabilities based on reported parameters (memory, processing power, network bandwidth) and use this feedback information to make informed selection decisions. The central node continuously monitors and adjusts selections based on the feedback from edge nodes about their resource availability, resolving the contradiction between processing capability and selection complexity.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS12517802B2Similarity-based quantization selection for federated learning with heterogeneous edge devices

Publication Date: 2026.01.06 DELL PROD LP

AI summary

One example method includes receiving, by a central node, respective data and statistics from each edge node in a group of edge nodes, performing, by the central node, a similarity-based clustering of the edge nodes so that different clusters of edge nodes are defined, sampling, by the central node, edge nodes from each of the clusters to perform a quantization selection method, receiving, by the central node, from the sampled edge nodes, a respective indication of a best-performing quantization method, and electing, by the central node, a quantization method to be used by all the edge nodes.