Cumulative Utility Parity for Fair Federated Learning under Intermittent Client Participation
arXiv:2602.13651v1 Announce Type: new Abstract: In real-world federated learning (FL) systems, client participation is intermittent, heterogeneous, and often correlated with data characteristics or resource constraints. Existing fairness approaches in FL primarily focus on equalizing loss or accuracy conditional on participation, implicitly assuming that clients have comparable opportunities to contribute over time. However, when participation itself is uneven, these objectives can lead to systematic under-representation of intermittently available clients, even if per-round performance appears fair. We propose cumulative utility parity, a fairness principle that evaluates whether clients receive comparable long-term benefit per participation opportunity, rather than per training round. To operationalize this notion, we introduce availability-normalized cumulative utility, which disentangles unavoidable physical constraints from avoidable algorithmic bias arising from scheduling and a
arXiv:2602.13651v1 Announce Type: new Abstract: In real-world federated learning (FL) systems, client participation is intermittent, heterogeneous, and often correlated with data characteristics or resource constraints. Existing fairness approaches in FL primarily focus on equalizing loss or accuracy conditional on participation, implicitly assuming that clients have comparable opportunities to contribute over time. However, when participation itself is uneven, these objectives can lead to systematic under-representation of intermittently available clients, even if per-round performance appears fair. We propose cumulative utility parity, a fairness principle that evaluates whether clients receive comparable long-term benefit per participation opportunity, rather than per training round. To operationalize this notion, we introduce availability-normalized cumulative utility, which disentangles unavoidable physical constraints from avoidable algorithmic bias arising from scheduling and aggregation. Experiments on temporally skewed, non-IID federated benchmarks demonstrate that our approach substantially improves long-term representation parity, while maintaining near-perfect performance.
Executive Summary
The article 'Cumulative Utility Parity for Fair Federated Learning under Intermittent Client Participation' addresses the challenge of fairness in federated learning (FL) systems where client participation is intermittent and heterogeneous. Traditional fairness approaches in FL focus on equalizing loss or accuracy per training round, assuming clients have comparable opportunities to contribute. However, this can lead to systematic under-representation of intermittently available clients. The authors propose a new fairness principle called cumulative utility parity, which evaluates whether clients receive comparable long-term benefit per participation opportunity. They introduce availability-normalized cumulative utility to disentangle physical constraints from algorithmic bias. Experiments on temporally skewed, non-IID federated benchmarks show that this approach improves long-term representation parity while maintaining high performance.
Key Points
- ▸ Intermittent client participation in FL systems leads to systematic under-representation of certain clients.
- ▸ Traditional fairness approaches in FL do not account for uneven participation opportunities.
- ▸ Cumulative utility parity evaluates long-term benefit per participation opportunity rather than per training round.
- ▸ Availability-normalized cumulative utility helps disentangle physical constraints from algorithmic bias.
- ▸ Experiments demonstrate improved long-term representation parity and near-perfect performance.
Merits
Novel Fairness Principle
The introduction of cumulative utility parity is a significant advancement in addressing fairness in FL systems with intermittent client participation. It provides a more nuanced and comprehensive approach to evaluating fairness over time.
Practical Implementation
The availability-normalized cumulative utility metric is a practical tool for operationalizing the fairness principle, making it easier to implement in real-world FL systems.
Empirical Validation
The experiments on temporally skewed, non-IID federated benchmarks provide strong empirical evidence supporting the effectiveness of the proposed approach.
Demerits
Complexity
The proposed approach adds complexity to the FL system, which may require additional computational resources and careful implementation to ensure it works effectively.
Generalizability
While the experiments show promising results, the generalizability of the approach to other types of FL systems and datasets needs further investigation.
Implementation Challenges
The practical implementation of availability-normalized cumulative utility may face challenges, particularly in systems with a large number of clients or highly variable participation patterns.
Expert Commentary
The article presents a significant advancement in the field of federated learning by addressing the critical issue of fairness under intermittent client participation. The introduction of cumulative utility parity is a novel and insightful approach that moves beyond traditional fairness metrics, which often overlook the long-term impact of uneven participation. The availability-normalized cumulative utility metric is a practical and effective tool for operationalizing this fairness principle, making it a valuable contribution to the field. The empirical validation through experiments on temporally skewed, non-IID federated benchmarks provides strong evidence of the approach's effectiveness. However, the complexity and potential implementation challenges should not be overlooked. Future research should focus on simplifying the approach and ensuring its generalizability across different FL systems and datasets. Overall, this article sets a new standard for addressing fairness in FL systems and provides a robust framework for future studies in this area.
Recommendations
- ✓ Further research should explore the scalability and generalizability of the proposed approach to different types of FL systems and datasets.
- ✓ Practical guidelines and tools should be developed to facilitate the implementation of availability-normalized cumulative utility in real-world FL systems.
- ✓ Policy makers should consider the findings of this study when developing regulations and guidelines for fair and equitable FL systems.
