Collaborative Fairness in Federated Learning (2008.12161v2)

Abstract: In current deep learning paradigms, local training or the Standalone framework tends to result in overfitting and thus poor generalizability. This problem can be addressed by Distributed or Federated Learning (FL) that leverages a parameter server to aggregate model updates from individual participants. However, most existing Distributed or FL frameworks have overlooked an important aspect of participation: collaborative fairness. In particular, all participants can receive the same or similar models, regardless of their contributions. To address this issue, we investigate the collaborative fairness in FL, and propose a novel Collaborative Fair Federated Learning (CFFL) framework which utilizes reputation to enforce participants to converge to different models, thus achieving fairness without compromising the predictive performance. Extensive experiments on benchmark datasets demonstrate that CFFL achieves high fairness, delivers comparable accuracy to the Distributed framework, and outperforms the Standalone framework.

• The paper proposes a reputation-based mechanism that aligns participant model updates with their contribution quality in federated learning.
• It demonstrates that CFFL significantly improves fairness, evidenced by correlation coefficients over 90% between contributions and rewards.
• Experiments on benchmark datasets like MNIST validate that CFFL matches or exceeds the performance of traditional methods like FedAvg.

Essay on "Collaborative Fairness in Federated Learning"

This paper addresses a critical yet overlooked aspect of Federated Learning (FL): collaborative fairness. Traditional FL frameworks operate under a paradigm where model parameters are aggregated from multiple participants to improve model generalizability. However, these aggregates do not consider the individual contributions of participants, which is a significant fairness concern. The paper proposes an innovative approach to address this imbalance through the Collaborative Fair Federated Learning (CFFL) framework, which incorporates a reputation-based mechanism to differentiate model dissemination based on contribution levels.

The authors highlight that the current FL systems provide uniform models to all participants, regardless of the varying quality and quantity of data each participant contributes. This one-size-fits-all approach can lead to discrepancies in incentives, particularly when the contributions of certain participants do not lead to a proportional benefit. By contrast, CFFL employs a participant reputation model evaluated through validation accuracy, which determines the distribution of the aggregated model updates. Such a system is intended to ensure that participants with greater contributions receive more beneficial updates, thus aligning model performance rewards with participant input.

CFFL represents a shift from traditional FL models by adapting the learning process to ensure fairness without sacrificing accuracy. Practically, CFFL is better suited to environments where contributions vary significantly among participants, such as financial or biomedical collaborations, where data heterogeneity and contribution imbalances are normatively prevalent.

In validating their approach, the authors conduct comprehensive experiments across benchmark datasets including MNIST and the Adult Census dataset. It is shown through empirical results that CFFL consistently achieves optimal collaborative fairness, evidenced by strong correlation coefficients (>90%) between contributions measured through standalone model accuracies and rewards evaluated through final model performance. The accuracy of the most contributive participant’s model in CFFL is observed to be comparable, if not superior, to standard FL approaches like FedAvg and DSSGD.

The paper is instrumental in offering a concrete mechanism to integrate fairness into FL, commonly an attribute associated with centralized frameworks. It stands out by adopting a reputation-based punishment mechanism that regulates participant contributions through iterative feedback, further refining model aggregation strategies. Moreover, the CFFL framework has demonstrated robustness against free-riders, enhancing its utility for real-world applications and further underscoring its practical significance.

The implications of this research are noteworthy in both theoretical and application domains. The concept of fairness in FL is sophisticated, demanding intricate balancing of contributions with rewards without deteriorating model performance. The CFFL framework sets the stage for further refinement of FL systems, potentially involving trust-based mechanisms for data sharing among heterogeneous and competitive environments.

Looking forward, potential enhancements might involve variance in fairness metrics or further dissect hybrid scenarios where imbalances span beyond data size and class distributions. A promising extension of this work is the systematic integration of adversarial robustness alongside fairness considerations, especially as federated systems encounter varied participant assumptions. Such developments will likely broaden the applicability of FL across multidisciplinary domains such as finance, healthcare, and social networks, where trust and equity in data utilization are critical.

In summary, the paper provides a rigorous exploration into the field of collaborative fairness in federated learning and presents a compelling framework that achieves fairness without compromising on model performance. The implications of this work are significant, laying groundwork for scalable and equitable FL systems that can adapt to the diverse needs of modern-day data collaborations.