Federated Learning Hyperparameter Tuning via Unified Metric Surface

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Solution Overview

Problem

Federated learning systems face challenges in dynamically optimizing hyperparameters, requiring manual iterations and lacking efficient methods for hyperparameter tuning across distributed models, which increases processing and communication overhead.

Innovation Solution

A computer-implemented method that issues a hyperparameter optimization query to multiple computing devices, receives results, generates a unified performance metric surface, and determines optimal global hyperparameters, allowing for automated tuning and reducing the need for manual adjustments.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual hyperparameter tuning is performed in federated learning, then hyperparameter optimization can be achieved, but processing time and communication overhead increase significantly

Engineering Contradiction:

Improvehyperparameter optimizationVSAvoidprocessing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs preliminary hyperparameter optimization by collecting HPO results from multiple computing devices and generating a unified performance metric surface in advance. This preliminary analysis enables the determination of optimal global hyperparameters before actual federated learning training begins, avoiding time-consuming manual tuning during the training process itself.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements self-service hyperparameter optimization where computing devices automatically perform HPO operations and return results to the aggregator. The aggregator then automatically generates the unified performance metric surface and determines optimal hyperparameters without human intervention, enabling the system to tune its own hyperparameters efficiently.

Inventive Principle:

Principle #25Self-service

2Measurement precision

If manual hyperparameter tuning is performed in federated learning, then hyperparameter optimization can be achieved, but communication overhead between devices increases

Engineering Contradiction:

Improvehyperparameter optimizationVSAvoidcommunication overhead

Core Design Contradiction:

Measurement precisionVSLoss of information

Solution Approach 1:

The system extracts only the essential HPO results from each computing device and returns them to the aggregator. By focusing on extracting relevant performance data rather than exchanging complete model information, the system reduces communication overhead while still achieving effective hyperparameter optimization through the unified performance metric surface.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

Instead of sharing actual training data or complete models between devices, the system uses copied performance metric information from HPO results. Each device generates local performance metrics that are copied and aggregated to form the unified performance metric surface, enabling hyperparameter optimization without direct data sharing or excessive communication.

Inventive Principle:

Principle #26Copying

3Productivity

If automated hyperparameter tuning is implemented, then processing efficiency improves, but system complexity increases

Engineering Contradiction:

Improveprocessing efficiencyVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system segments the hyperparameter optimization process into distinct components: individual computing devices perform local HPO operations independently, the aggregator collects and combines results to generate the unified performance metric surface, and optimal hyperparameters are determined from this surface. This segmentation allows automated tuning without requiring complex interactions between all components.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The unified performance metric surface acts as an intermediary that bridges local HPO results from multiple devices and the final optimal global hyperparameters. This intermediary structure simplifies the system by providing a clear mediation mechanism that transforms distributed local results into centralized optimization decisions without requiring direct complex interactions between all computing devices.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS20230186168A1Performing automated tuning of hyperparameters in a federated learning environment

Publication Date: 2023.06.15 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

A computer-implemented method according to one embodiment includes issuing a hyperparameter optimization (HPO) query to a plurality of computing devices; receiving HPO results from each of the plurality of computing devices; generating a unified performance metric surface utilizing the HPO results from each of the plurality of computing devices; and determining optimal global hyperparameters, utilizing the unified performance metric surface.