Tokenized Federated Learning Incentive Mechanism
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Solution Overview
Problem
Conventional federated learning platforms fail to incentivize data parties for their contributions, leading to limited participation and collaboration, as they do not compensate parties for their data and resources effectively.
Innovation Solution
A token-based system is implemented where data parties are allocated tokens based on their data usage and participation profiles, with new tokens allocated and existing tokens reimbursed based on the accuracy of the machine learning model, encouraging participation and collaboration among data parties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional federated learning platforms are used without token incentives, then the system structure remains simple, but data party participation and collaboration are limited
Solution Approach 1:
The patent introduces a token as an intermediary incentive mechanism between the federated learning platform and data parties. The token serves as a mediator that quantifies and rewards data party contributions, thereby increasing participation without fundamentally altering the federated learning architecture. The token system acts as a virtual currency that facilitates collaboration by compensating data parties for their resource contributions.
Solution Approach 2:
The patent changes the incentive parameter from none to token-based rewards. By introducing tokens as a measurable and tradable incentive parameter, the system transforms data party motivations from voluntary to economically driven participation. The token allocation and reimbursement mechanisms create a dynamic incentive structure that adapts to data party contributions and model accuracy improvements.
2Productivity
If data parties are not compensated for their data and resources, then the system operates with fewer components, but collaboration and frequent participation are not promoted
Solution Approach 1:
The patent implements a feedback loop where data parties receive token reimbursements based on their contributions and the resulting model accuracy improvements. The aggregator platform continuously monitors data party contributions, calculates token reimbursements, and distributes them accordingly. This feedback mechanism creates a closed-loop incentive system that reinforces continued participation and collaboration by directly rewarding measurable contributions.
3Productivity
If tokens are allocated and reimbursed based on data usage and model accuracy, then data party incentives are improved, but token management complexity increases
Solution Approach 1:
The patent implements a self-service token management system where data parties automatically receive token allocations and reimbursements based on their contributions. The aggregator platform automatically tracks data usage, calculates token earnings, and distributes reimbursements without requiring manual intervention. This self-service approach reduces the operational complexity of token management while maintaining effective incentives.
Data Source
AI summary
One embodiment of the invention provides a method for federated learning (FL) comprising training a machine learning (ML) model collaboratively by initiating a round of FL across data parties. Each data party is allocated tokens to utilize during the training. The method further comprises maintaining, for each data party, a corresponding data usage profile indicative of an amount of data the data party consumed during the training and a corresponding participation profile indicative of an amount of data the data party provided during the training. The method further comprises selectively allocating new tokens to the data parties based on each participation profile maintained, selectively allocating additional new tokens to the data parties based on each data usage profile maintained, and reimbursing one or more tokens utilized during the training to the data parties based on one or more measurements of accuracy of the ML model.