RL-Driven Gradient Compression for Distributed Model Training

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Solution Overview

Problem

Distributed and federated learning environments face challenges in managing data transmission, particularly the transmission of large gradient updates from client devices to a server, which consumes significant resources such as power, compute, and network resources.

Innovation Solution

Implementing dynamic gradient compression using reinforcement learning to optimize resource consumption, where client devices generate gradients and compress them based on parameters determined by an RL agent server, reducing the data sent over the network.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If large gradient updates are transmitted from client devices to server, then model training accuracy is improved, but network bandwidth consumption and resource usage increase significantly

Engineering Contradiction:

Improvemodel training accuracyVSAvoiddata transmission volume

Core Design Contradiction:

ReliabilityVSQuantity of substance

Solution Approach 1:

The patent extracts only the most important gradient information by applying compression techniques that retain essential gradient components while discarding redundant data. This allows the server to receive minimized gradient updates that preserve training accuracy while significantly reducing transmission volume.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The patent dynamically adjusts compression parameters based on gradient magnitude and importance. By changing compression strength and precision levels adaptively, the system optimizes the balance between transmission size and information retention, ensuring accurate model updates with minimal data transfer.

Inventive Principle:

Principle #35Parameter changes

2Use of energy by moving object

If gradient compression parameters are adjusted dynamically, then resource consumption is optimized, but system complexity increases due to reinforcement learning integration

Engineering Contradiction:

Improveresource consumptionVSAvoidsystem complexity

Core Design Contradiction:

Use of energy by moving objectVSDevice complexity

Solution Approach 1:

The system implements self-service by using reinforcement learning agents that automatically learn optimal compression strategies without manual intervention. The agents continuously adapt compression parameters based on training feedback, eliminating the need for complex manual configuration while optimizing resource usage.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent incorporates feedback mechanisms where compression performance is continuously evaluated and used to refine future compression decisions. This closed-loop approach allows the system to learn from past performance and automatically adjust parameters to reduce resource consumption while maintaining training quality.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20260030508A1Dynamic compression by reinforcement learning in a distributed learning environment

Publication Date: 2026.01.29 VMWARE INC

AI summary

An example device includes: a first system configured to implement a model having first parameters, generate gradients for the first parameters in response to training the model on first data sets, and compress the gradients based on second parameters; and circuits in the first system, the circuits including a network interface controller. The first system is further configured to receive updates to the second parameters from a second system through the network interface controller coupled to a network, send the gradients as compressed to a third system through the network interface controller, and apply the updates to the second parameters to adjust resource consumption of at least one of the circuits.