Federated Learning Equity Reward Distribution via Adams Theory
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Solution Overview
Problem
Existing federated learning incentive mechanisms fail to provide equitable rewards, leading to free-riding and discouragement of high-quality participants due to their inability to account for various inputs beyond contribution, such as enthusiasm, experience, and tolerance, which are essential for psychological equity.
Innovation Solution
A method applying Adams' equity theory to dynamically adjust weights of data contribution, model contribution, and waiting-time allowance to ensure actual rewards align with expected rewards, utilizing a blockchain for transparency and credibility, thereby ensuring participants' perceived equity and motivation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If equal rewards are distributed to each participant, then simplicity of reward distribution is improved, but participant motivation deteriorates due to perceived inequity ignoring differences in data, resources, and contributions
Solution Approach 1:
The patent applies local quality by distributing rewards differently to different participants based on their individual characteristics. Instead of uniform treatment, the system evaluates each participant's data quality, contribution level, and resource investment, then allocates rewards proportionally to create localized equity for each participant group.
Solution Approach 2:
The patent changes the reward distribution parameter from a fixed equal amount to a dynamic value based on multiple factors including data quality metrics, contribution rates, and resource investments. This parameter transformation allows the system to adapt rewards to individual participant characteristics while maintaining overall system fairness.
2Reliability
If rewards are distributed based on contribution rate, then participant motivation is improved through equity perception, but free-riding attacks worsen as participants exploit the system without adequate contribution
Solution Approach 1:
The patent implements feedback mechanisms by continuously monitoring participant contributions, evaluating data quality metrics, and adjusting reward allocations accordingly. The system provides feedback to participants about their contribution levels and expected rewards, creating a closed-loop system that discourages free-riding while maintaining motivation for genuine contributors.
Solution Approach 2:
The patent applies dynamics by making the reward distribution system adaptive and flexible. Rather than fixed contribution-reward ratios, the system dynamically adjusts weights and evaluation criteria based on participant behavior, data quality variations, and system needs, allowing it to respond to free-riding attempts while maintaining equity for legitimate participants.
3Reliability
If multiple evaluation factors are considered for reward distribution, then equity perception is improved by accounting for diverse inputs, but system complexity worsens due to multiple evaluation dimensions
Solution Approach 1:
The patent segments the complex evaluation process into distinct modular components: data quality evaluation, contribution rate assessment, resource investment measurement, and reward calculation modules. Each module handles a specific aspect independently, making the overall complex system manageable and maintainable while comprehensively evaluating multiple factors for equitable reward distribution.
Data Source
AI summary
A method for distributing an equity reward for federated learning based on an equity theory includes the following steps: applying Adams' equity theory to federated learning, analyzing, by a participant, all factors invested in a federated task comprehensively, then giving an expected reward for this task, calculating, by the task publisher, the reputation of the participant; participating, by the participant, in each round of a training task using a local data to evaluate data contribution, model contribution, and a waiting-time allowance of the participant, then combining contribution results of the three factors to evaluate the contribution of the participant; after a global model converges, dynamically adjusting weights of the three factors according to an objective function of the equity reward, with a goal that an actual reward of the participant is as close as possible to the expected reward, and obtaining and distributing the actual reward of the participant.