Time-Aware Incentive Framework for Federated Learning During Critical Periods
A paper published on arXiv introduces an incentive framework based on contract theory, known as Right Reward Right Time (R3T), aimed at tackling critical learning periods (CLPs) in federated learning (FL). CLPs represent early phases where contributions of poor quality, like sparse training data, can have a lasting negative impact on the global model’s effectiveness. Current incentive structures overlook the varying significance of training rounds, treating them all the same and neglecting the need for high-quality inputs during CLPs. The issue is compounded by information asymmetry from privacy laws, which leaves the cloud unaware of client training capabilities, resulting in adverse selection and moral hazard. R3T promotes client participation in CLPs to enhance cloud utility, establishing a utility function that balances model performance with incentives.
Key facts
- Critical learning periods (CLPs) are early stages in federated learning where low-quality contributions can permanently impair the global model.
- Existing incentive mechanisms assume temporal homogeneity, treating all training rounds equally.
- Information asymmetry due to privacy regulations leads to adverse selection and moral hazard.
- The proposed framework is named Right Reward Right Time (R3T).
- R3T is a time-aware contract-theoretic incentive framework.
- R3T aims to encourage client involvement during CLPs.
- The cloud utility function captures the trade-off between model performance and incentives.
- The paper is available on arXiv with ID 2503.07869.
Entities
Institutions
- arXiv