Federated Learning Platform Incentive Model
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Solution Overview
Problem
Current machine learning (ML) model training is constrained by the availability of data, as entities may be unwilling or legally restricted from sharing certain data due to privacy concerns, making it difficult to obtain sufficient training data for accurate predictions.
Innovation Solution
A federated learning platform that enables the creation of global ML models by aggregating model parameters from multiple clients training local models on their own data, while providing incentives for data contribution, thus allowing for improved model performance without sharing raw data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If more data is shared for training ML models, then model accuracy is improved, but data privacy and legal compliance are compromised
Solution Approach 1:
The system segments the training process into distributed local models at different client sites, where each client trains a local model using its own data. Only model parameters are shared with the central server, not the raw data itself. This segmentation allows model accuracy to improve through aggregated learning while data privacy is preserved at each client site.
Solution Approach 2:
The patent introduces model parameters as an intermediary between raw data and the global model. Instead of sharing raw data directly, the system exchanges and aggregates model parameters from multiple clients. This intermediary mechanism enables the central server to build an accurate global model while clients maintain control over their private data, resolving the privacy-accuracy contradiction.
2Measurement precision
If data is centralized for training, then model performance is improved, but data transfer requirements and system complexity increase
Solution Approach 1:
Instead of centralizing data to train models (the conventional approach), the system inverts the approach by distributing model training to local clients and only aggregating the resulting parameters centrally. This inversion reduces data transfer requirements and system complexity while maintaining model performance through the aggregated parameters from multiple distributed sources.
3Quantity of substance
If clients share raw data for training, then training data availability is improved, but incentive motivation is reduced
Solution Approach 1:
The system creates and exchanges copies of model parameters instead of raw data. Clients train local models and share parameter copies with the central server, which aggregates these copies to form the global model. This copying mechanism increases training data availability through parameter aggregation while maintaining client incentive motivation, as clients are compensated for contributing their computational resources and model parameters without sacrificing data privacy.
Data Source
AI summary
Architectures, apparatuses and methods for building data learning systems (e.g., machine learning (ML) systems, etc.). In some embodiments, an architecture to build a global machine learning (ML) model includes a platform to identify a group of clients to build a global model by federated learning. In some embodiments, the platform includes a group manager to build the global model by supplying a model definition for the global model to the group and aggregating model parameters received from the group to build the global model, the model parameters being generated by the clients training a local ML model at their respective client sites using local data at their respective client sites; and an incentive calculator communicably coupled to the group manager to calculate an incentive to each client communicably coupled to the platform based on said each client's contribution to train the global model, each client's contribution including one or more model parameters generated as a result of training their local ML model with local data.