Edge-Weighted Quantization for Federated Learning Convergence

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Solution Overview

Problem

Federated learning systems face challenges in balancing bandwidth requirements and convergence rate, with aggressive gradient compression leading to poor model convergence performance, while non-quantized updates may result in faster convergence but higher communication costs.

Innovation Solution

A method for dynamically selecting edge nodes to perform quantization procedures based on estimated convergence rates, using a central node to aggregate and select the best compression algorithm for the federation, while minimizing additional processing overhead on edge nodes.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Quantity of substance

If aggressive gradient compression is used, then bandwidth requirements are reduced, but convergence rate deteriorates

Engineering Contradiction:

Improvebandwidth consumptionVSAvoidconvergence rate

Core Design Contradiction:

Quantity of substanceVSSpeed

Solution Approach 1:

The system dynamically adjusts the quantization procedure based on estimated convergence rates. Different edge nodes may use different quantization levels at different times, allowing the system to optimize the trade-off between bandwidth consumption and convergence rate adaptively rather than using a fixed compression level for all nodes

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

Different edge nodes are assigned different quantization procedures based on their local conditions and convergence characteristics. This allows each node to use an appropriate compression level for its specific data and model state, rather than applying uniform compression across all nodes

Inventive Principle:

Principle #3Local quality

2Speed

If non-quantized updates are used, then convergence rate is improved, but communication costs increase

Engineering Contradiction:

Improveconvergence rateVSAvoidcommunication cost

Core Design Contradiction:

SpeedVSQuantity of substance

Solution Approach 1:

The system changes the quantization parameter dynamically based on the estimated convergence rate. When convergence is slow, less aggressive quantization (or no quantization) is applied. When convergence is fast, more aggressive quantization is applied, thus adapting the communication cost to the actual convergence needs

Inventive Principle:

Principle #35Parameter changes

3Reliability

If quantization procedure is selected based on convergence rate estimation, then model convergence performance is improved, but additional processing overhead is introduced

Engineering Contradiction:

Improveconvergence performanceVSAvoidprocessing overhead

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system performs convergence rate estimation and quantization selection only on a sampled subset of edge nodes rather than all nodes. This partial application of the complex selection procedure reduces the overall processing overhead while still improving convergence performance for the federation

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS12518220B2Edge-weighted quantization for federated learning

Publication Date: 2026.01.06 DELL PROD LP

AI summary

Techniques are disclosed for dynamic edge-weighted quantization. For example, a system can include at least one processing device including a processor coupled to a memory, the at least one processing device being configured to implement the following steps: selecting edge nodes for sampling based on an edge node sampling algorithm configured to use a specified number of edge nodes to be sampled; causing the selected edge nodes to execute a quantization selection procedure; receiving, from the selected edge nodes, identifications of a quantization procedure based on the quantization selection procedure; and selecting a quantization procedure for each edge node, based on the identifications of the quantization procedures for the selected edge nodes.