Información - Machine Learning - # Fair Federated Learning

WassFFed: Achieving Fairness in Federated Learning using Wasserstein Barycenters

Q: Could the reliance on a global Wasserstein barycenter potentially limit the ability of individual clients to address specific fairness issues present in their local data?

Yes, relying solely on a global Wasserstein barycenter in WassFFed could potentially hinder a client's ability to address unique fairness issues present in its local data. Here's why: Data Heterogeneity: Federated learning often involves diverse clients with varying data distributions. A global barycenter represents an "average" fairness target, which might not adequately address specific biases amplified in a particular client's dataset. Over-Correction: A client with a relatively fair local model might be forced to adjust its model significantly to align with a global barycenter influenced by other clients with more pronounced fairness issues. This over-correction could negatively impact the client's local performance without necessarily improving global fairness. Contextual Fairness: Fairness is not always one-size-fits-all. What's considered fair in one context might not be in another. A global barycenter might not capture these nuances, potentially leading to unfair outcomes for certain clients. Mitigations: Personalized Fairness Targets: Instead of a single global barycenter, consider allowing for personalized fairness targets for each client. These targets could be derived by combining the global barycenter with a client-specific fairness objective, allowing for a balance between global alignment and local fairness considerations. Fairness Regularization: Incorporate fairness as a regularization term in each client's local loss function. This would allow clients to optimize for both accuracy and fairness on their local data while still being guided towards the global fairness objective. Hierarchical Barycenters: Explore a hierarchical approach with both global and local barycenters. Clients could first align with a local barycenter computed among a cluster of similar clients, followed by a weaker alignment with the global barycenter.

Conceptos Básicos

WassFFed is a novel framework that enhances fairness in federated learning by minimizing discrepancies in model outputs for different sensitive groups across clients using Wasserstein barycenters and optimal transport.

Resumen

Bibliographic Information: Han, Z., Zhang, L., Chen, C., Zheng, X., Zheng, F., Li, Y., & Yin, J. (2015). WassFFed: Wasserstein Fair Federated Learning. Journal of LaTeX Class Files, 14(8), 1-7.
Research Objective: This paper introduces WassFFed, a novel framework designed to address the challenges of achieving fairness in federated learning (FL), particularly in scenarios with non-IID data distributions across clients.
Methodology: WassFFed operates by calculating a global Wasserstein barycenter based on the output distributions of local models for different sensitive groups. It then utilizes optimal transport matrices to guide the output distributions of each client towards this barycenter, thereby minimizing discrepancies and promoting fairness. The framework incorporates differential privacy measures to protect user data.
Key Findings: Experimental results on three real-world datasets demonstrate that WassFFed outperforms existing fair FL methods in balancing accuracy and fairness, particularly in complex classification tasks. The framework effectively reduces disparities in model outputs for different sensitive groups, indicating its ability to mitigate bias.
Main Conclusions: WassFFed offers a promising solution for achieving fairness in FL by addressing the limitations of surrogate functions and handling non-IID data distributions. The use of Wasserstein barycenters and optimal transport enables efficient and privacy-preserving fairness optimization.
Significance: This research contributes significantly to the field of fair machine learning by introducing a novel and effective framework for addressing fairness concerns in the context of FL, which is crucial for real-world applications with decentralized data.
Limitations and Future Research: The paper acknowledges the need for further investigation into the impact of varying data heterogeneity levels on WassFFed's performance. Exploring the framework's applicability to other fairness notions beyond demographic parity and equal opportunity is also suggested.

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WassFFed: Wasserstein Fair Federated Learning

by Zhongxuan Ha... a las arxiv.org 11-12-2024

https://arxiv.org/pdf/2411.06881.pdf

WassFFed: Wasserstein Fair Federated Learning

Consultas más profundas

How might the WassFFed framework be adapted to address fairness concerns in other decentralized learning paradigms beyond federated learning?

The core principles of WassFFed, centered around minimizing discrepancies in model output distributions using Wasserstein barycenters, hold promise for adaptation to other decentralized learning paradigms. Here's how:

Decentralized Learning with Blockchain: In blockchain-based learning, where data and model updates are distributed across a network of nodes, WassFFed could be integrated into the consensus mechanism. Each node could calculate its local output distributions and share them securely. A global Wasserstein barycenter could be computed in a privacy-preserving manner using techniques like secure multi-party computation, and nodes could adjust their models accordingly.

Split Learning: This paradigm partitions the model architecture across a client and server. WassFFed could be applied by treating the client-side output as one distribution and the server-side output as another. Minimizing the Wasserstein distance between these distributions would promote fairness across the partitioned model.

Peer-to-Peer Federated Learning:  In this setting, clients communicate directly without a central server. WassFFed could be implemented by having clients exchange their output distributions with their peers. Each client could then calculate a local Wasserstein barycenter based on its neighborhood and adjust its model to align with this local barycenter.
Key Considerations for Adaptation:

Communication Efficiency:  Decentralized environments often have communication constraints. Adapting WassFFed would require exploring efficient methods for exchanging output distributions and barycenter information.

Privacy Preservation:  Ensuring data privacy is paramount. Techniques like differential privacy, homomorphic encryption, or secure aggregation would be crucial for protecting sensitive information during barycenter computation and model updates.

Robustness to Adversarial Behavior:  In decentralized settings, malicious actors could attempt to manipulate the barycenter calculation. Robust aggregation methods and anomaly detection would be essential.

Could the reliance on a global Wasserstein barycenter potentially limit the ability of individual clients to address specific fairness issues present in their local data?

Yes, relying solely on a global Wasserstein barycenter in WassFFed could potentially hinder a client's ability to address unique fairness issues present in its local data. Here's why:

Data Heterogeneity: Federated learning often involves diverse clients with varying data distributions. A global barycenter represents an "average" fairness target, which might not adequately address specific biases amplified in a particular client's dataset.

Over-Correction:  A client with a relatively fair local model might be forced to adjust its model significantly to align with a global barycenter influenced by other clients with more pronounced fairness issues. This over-correction could negatively impact the client's local performance without necessarily improving global fairness.

Contextual Fairness: Fairness is not always one-size-fits-all. What's considered fair in one context might not be in another. A global barycenter might not capture these nuances, potentially leading to unfair outcomes for certain clients.
Mitigations:

Personalized Fairness Targets: Instead of a single global barycenter, consider allowing for personalized fairness targets for each client. These targets could be derived by combining the global barycenter with a client-specific fairness objective, allowing for a balance between global alignment and local fairness considerations.

Fairness Regularization: Incorporate fairness as a regularization term in each client's local loss function. This would allow clients to optimize for both accuracy and fairness on their local data while still being guided towards the global fairness objective.

Hierarchical Barycenters: Explore a hierarchical approach with both global and local barycenters. Clients could first align with a local barycenter computed among a cluster of similar clients, followed by a weaker alignment with the global barycenter.

How can the trade-off between fairness and accuracy be dynamically adjusted within the WassFFed framework based on the specific requirements of the application domain?

Dynamically adjusting the fairness-accuracy trade-off in WassFFed is crucial for tailoring the framework to different application domains. Here are some strategies:
1. Adaptive Balancing Parameter (β):

Contextual Feedback:  Continuously monitor the model's performance on both fairness and accuracy metrics. If fairness lags significantly, decrease β to emphasize fairness. Conversely, if accuracy suffers, increase β to prioritize accuracy.

Domain-Specific Constraints: Define acceptable thresholds for fairness and accuracy based on the application domain. Adjust β dynamically to ensure the model operates within these constraints. For instance, in healthcare, fairness might be paramount, even at a slight cost to accuracy.
2. Multi-Objective Optimization:

Pareto Optimization:  Instead of a single solution, explore the Pareto frontier of solutions representing different trade-offs between fairness and accuracy. This allows for selecting the most suitable solution based on the specific application requirements.

Constrained Optimization:  Formulate the problem as a constrained optimization, where fairness metrics act as constraints. The optimization process then aims to maximize accuracy while satisfying the fairness constraints.
3. Client-Specific Trade-offs:

Preference Elicitation: Allow clients to specify their preferences for the fairness-accuracy trade-off. This is particularly relevant in scenarios where clients have different sensitivities to fairness concerns.

Federated Multi-Task Learning:  Treat fairness and accuracy as separate tasks within a federated multi-task learning framework. This allows for learning task-specific models and adjusting their relative importance based on the application domain.
Practical Considerations:

Monitoring and Evaluation:  Implement robust monitoring and evaluation mechanisms to track both fairness and accuracy metrics over time. This provides insights into the impact of dynamic adjustments.

Explainability:  Ensure transparency in how the trade-off is being managed. Provide clear explanations for the chosen fairness-accuracy balance to build trust and accountability.

Ethical Review:  Engage in ethical considerations and involve domain experts in defining acceptable trade-offs, especially in sensitive application domains.