Decentralized ML Model Update via Stale Straggler Data

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Solution Overview

Problem

Decentralized learning of machine learning models is hindered by straggler devices that generate and transmit updates at different rates, leading to sub-optimal global model updates due to waiting for stale updates, resulting in biased models and inefficient resource utilization.

Innovation Solution

Implementing techniques such as Federated Asynchronous Regularization with Distillation Using Stale Teachers (FARe-DUST) and Federated Asynchronous Straggler Training on Mismatched and Stale Gradients (FeAST on MSG), which allow the remote system to proceed with updates without waiting for all devices, utilizing stale updates asynchronously to generate historical or auxiliary models that incorporate knowledge from straggler devices.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If the remote system waits for all client devices to complete updates before proceeding with global model updates, then the global model update completeness is improved, but the update speed and productivity deteriorate due to straggler devices

Engineering Contradiction:

Improveglobal model update completenessVSAvoidupdate speed

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The remote system performs preliminary actions by updating the global model with available updates before all client devices have completed their updates. This allows the system to proceed with model improvement using current data while straggler devices continue computing, eliminating the waiting time that previously hindered productivity.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system maintains continuous useful action by continuously accepting and applying updates as they arrive from client devices, rather than stopping to wait for all devices. This continuous update process ensures that the global model is constantly improving without idle periods caused by straggler devices.

Inventive Principle:

Principle #20Continuity of useful action

2Productivity

If the remote system discards stale updates from straggler devices, then the update efficiency is improved, but the model bias and representation quality worsen

Engineering Contradiction:

Improveupdate efficiencyVSAvoidmodel representation quality

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system introduces an intermediary mechanism that tracks and reconciles updates from straggler devices. This intermediary layer allows the system to maintain efficiency by not waiting for stragglers while ensuring their updates are eventually incorporated, preventing model bias and maintaining representation quality across all data domains.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system changes the parameter of update acceptance from binary (wait for all or discard) to a continuous spectrum where updates are accepted at different times based on their arrival. This parameter change allows the system to maintain both efficiency and representation quality by flexibly incorporating updates from all devices regardless of their speed.

Inventive Principle:

Principle #35Parameter changes

3Speed

If client devices dedicate more computational resources to generating updates, then the update generation speed is improved, but the resource consumption and energy use worsen

Engineering Contradiction:

Improveupdate generation speedVSAvoidcomputational resource consumption

Core Design Contradiction:

SpeedVSUse of energy by moving object

Solution Approach 1:

The system applies partial action by allowing each client device to contribute updates at its own resource capacity rather than requiring all devices to dedicate maximum resources. This partial contribution approach maintains overall update generation speed while preventing excessive resource consumption and energy use across the distributed system.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS20240095582A1Decentralized learning of machine learning model(s) through utilization of stale updates(s) received from straggler computing device(s)

Publication Date: 2024.03.21 GOOGLE LLC

AI summary

During a round of decentralized learning for updating of a global machine learning (ML) model, remote processor(s) of a remote system may transmit, to a population of computing devices, primary weights for a primary version of the global ML model, and cause each of the computing devices to generate a corresponding update for the primary version of the global ML model. Further, the remote processor(s) may cause the primary version of the global ML model to be updated based on the corresponding updates that are received during the round of decentralized learning. However, the remote processor(s) may receive other corresponding updates subsequent to the round of decentralized learning. Accordingly, various techniques described herein (e.g., FARe-DUST, FeAST on MSG, and/or other techniques) enable the other corresponding updates to be utilized in achieving a final version of the global ML model.