Federated Learning Incentive Calculation for Privacy-Preserving Model Updates
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Solution Overview
Problem
In federated learning, clients participating in the model training do not receive adequate incentives based on their contribution to the model's accuracy, leading to a lack of motivation for significant contributions while maintaining data privacy and security.
Innovation Solution
An information processing system that calculates and provides incentives to clients based on their contribution degree to the global model by receiving local models or output data from multiple nodes, updating the global model, and evaluating the contribution of each local model or output data to the updated global model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If federated learning is used to train global model with data privacy and security, then data privacy and security are improved, but clients do not receive adequate incentives based on their contribution to model accuracy
Solution Approach 1:
The system calculates and provides feedback to each client about their contribution degree to the global model accuracy. This feedback mechanism motivates clients by showing them their specific impact, while the federated learning architecture maintains data privacy and security by keeping data local. The feedback loop closes by using this contribution information to adjust incentives or recognition.
2Reliability
If local data is kept distributed to ensure data privacy and security, then data privacy and security are improved, but contribution degree calculation becomes more difficult
Solution Approach 1:
The system introduces an intermediary mechanism (contribution degree calculation module) that mediates between the distributed local data and the global model. This intermediary calculates contribution degrees by analyzing model updates, gradients, or performance metrics without requiring access to actual local data, thus maintaining privacy while enabling accurate contribution measurement.
Solution Approach 2:
The system replaces direct mechanical data aggregation with a computational approach using model parameters, gradients, or performance indicators to infer contribution degrees. Instead of mechanically summing up data contributions, the system uses mathematical operations on model representations to substitute for direct data access, enabling contribution measurement without data centralization.
3Reliability
If clients participate in federated learning without adequate incentive, then data privacy and security are maintained, but client participation and contribution are insufficient
Solution Approach 1:
The system implements a feedback mechanism that calculates and communicates contribution degrees to participating clients. This feedback serves as an incentive by recognizing and rewarding clients based on their actual contribution to model accuracy, thereby increasing motivation for participation while the federated learning framework maintains data privacy and security.
Solution Approach 2:
The system changes the parameter of client motivation by introducing contribution degree as a measurable and rewardable parameter. By transforming the abstract concept of contribution into a quantifiable parameter that can be tracked and rewarded, the system increases client participation and contribution while maintaining the privacy-preserving federated learning architecture.
Data Source
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AI summary
An information processing apparatus, a node, an information processing method, a carrier means, and an information processing system. The information processing apparatus receives information indicating a local model or output data, from each of a plurality of nodes, the information indicating the local model being obtained by learning a local data processed by the node based on a global model, the output data being obtained by inputting shared data to the local model, updates the global model based on a plurality of the information indicating the local model or a plurality of the output data received from the plurality of nodes, and calculates a contribution degree of each of the plurality of local models or each of the plurality of output data to the updated global model.