Federated Learning Marketplace with Revenue Sharing
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Solution Overview
Problem
There is a need for improved methods to implement and provide trained machine and deep learning models to users, particularly in biomedical and healthcare applications, where acquiring large training sets is challenging.
Innovation Solution
A federated learning marketplace is established with a central coordinator that orchestrates the training of machine learning and deep learning models on client devices without sharing private datasets, allowing clients to form coalitions, share model parameters, and receive revenue streams from model consumers, while ensuring secure aggregation and encryption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional centralized training is used to acquire large training sets, then model training quality improves, but data privacy and security deteriorate
Solution Approach 1:
The training process is segmented into local training at individual institutions and centralized aggregation at the coordinator. Each institution trains models locally on its own private data, then only model parameters (not raw data) are shared with the central coordinator for aggregation. This segmentation allows model training quality to improve through diverse data sources while data privacy is maintained by keeping sensitive information localized.
Solution Approach 2:
The central coordinator acts as an intermediary that receives encrypted model parameters from participating institutions, aggregates them using secure aggregation protocols, and distributes updated global models back to participants. This intermediary mechanism enables collaborative training across multiple institutions without requiring direct data sharing, thus improving model quality while maintaining data privacy through cryptographic protection.
2Object-affected harmful factors
If federated learning is implemented across geographically distributed institutions, then data privacy is maintained, but system complexity increases
Solution Approach 1:
The central coordinator is designed as a universal platform that handles multiple functions: orchestrating training rounds, aggregating model parameters from diverse institutions, managing encryption and decryption operations, distributing updated models, and handling revenue distribution. This multi-functional design simplifies the overall system architecture by consolidating coordination complexity into a single centralized component rather than requiring complex peer-to-peer coordination between all institutions.
Solution Approach 2:
The system implements feedback loops where the central coordinator monitors training progress, convergence criteria, and performance metrics from participating institutions. Based on this feedback, the coordinator adjusts training parameters, determines when convergence is achieved, and manages the synchronization process. This feedback mechanism automates the coordination complexity, reducing the need for manual intervention while maintaining data privacy through encrypted communications.
3Productivity
If model parameters are shared for aggregation, then collaborative training improves, but security requirements increase
Solution Approach 1:
The system transforms the sharing of sensitive data into sharing of model parameters (weights and biases) that have already been trained on local data. This parameter transformation allows collaborative training to proceed efficiently since parameters are compact and can be aggregated mathematically, while security requirements are managed through encryption protocols that protect these parameters during transmission and aggregation. The parameter change from raw data to model parameters fundamentally reduces the security burden while maintaining collaborative efficiency.
Data Source
AI summary
A federated learning environment includes a central coordinator that is responsible to orchestrate execution of the federated learning environment, and a plurality of clients jointly that trains machine learning and deep learning models on client computing devices without sharing their local private datasets. The clients only share their locally trained model parameters with the central coordinator. Model parameters are encrypted before sharing with the controller. The central coordinator aggregates local models and computes a new global model in encrypted space. This process repeats for a number of synchronization periods or asynchronously until specific convergence criteria are met. A federated learning marketplace is established to incentivize data providers to join federations through a revenue-sharing model, and to facilitate the use of machine-learning models to organizations outside of the federation.