Ditto: Fair and Robust Federated Learning Through Personalization

Abstract: Fairness and robustness are two important concerns for federated learning systems. In this work, we identify that robustness to data and model poisoning attacks and fairness, measured as the uniformity of performance across devices, are competing constraints in statistically heterogeneous networks. To address these constraints, we propose employing a simple, general framework for personalized federated learning, Ditto, that can inherently provide fairness and robustness benefits, and develop a scalable solver for it. Theoretically, we analyze the ability of Ditto to achieve fairness and robustness simultaneously on a class of linear problems. Empirically, across a suite of federated datasets, we show that Ditto not only achieves competitive performance relative to recent personalization methods, but also enables more accurate, robust, and fair models relative to state-of-the-art fair or robust baselines.

• The paper introduces Ditto, a multi-task framework that personalizes models to improve fairness and robustness in heterogeneous federated systems.
• It employs a regularization strategy that steers device-specific models toward a global model, reducing accuracy variance by approximately 10%.
• Empirical results reveal that Ditto boosts test accuracy by around 6% over robust baselines while effectively mitigating adversarial attacks.

An Analysis of Ditto: Fairness and Robustness in Federated Learning

This paper addresses the inherent challenges faced by federated learning (FL) systems in achieving fairness and robustness, especially in the presence of statistically heterogeneous data and potential adversarial attacks. The authors center their study around Ditto, a framework introduced to personalize federated learning by balancing competing constraints and enabling superior model performance.

Key Contributions

1. Competing Constraints in FL: The paper delineates the tension between fairness and robustness in FL models. Fairness typically seeks uniform performance across devices, whereas robustness aims to protect the system from training-time attacks such as data or model poisoning. These constraints often conflict, leading to vulnerabilities or disparities in performance when optimizing a single global model.
2. The Ditto Framework: Ditto is introduced as a scalable multi-task learning framework for FL. This framework allows personalized models for individual devices, shifting from a purely global objective approach. Ditto effectively incorporates a regularization term that nudges device-specific models towards a global model, thus managing heterogeneity and ensuring both fairness and robustness.
3. Theoretical Analysis: The paper provides a theoretical foundation showing how Ditto can simultaneously improve fairness and robustness. These benefits are particularly examined on a class of linear problems, demonstrating that personalized FL models can mitigate the antagonistic relationship between fairness and robustness. This theory hinges on predicating optimal personalization strategies that adapt as per model and data distribution characteristics.
4. Empirical Validation: The authors evaluate Ditto on multiple federated datasets, contrasting its performance against both state-of-the-art personalization techniques and robust aggregation methods. Empirical results suggest that Ditto significantly boots accuracy, robustness, and fairness across diverse FL settings—even outperforming solutions specifically tailored for either robustness or fairness individually. It achieves these improvements with an average test accuracy boost of approximately 6% over the strongest robust baselines.
5. Augmentation and Modularity: Ditto showcases inherent modularity, allowing integration with existing global FL methods. For instance, robust global aggregation techniques (like robust aggregators) can be embedded within Ditto to enhance model resilience while maintaining the personalization benefits.

Results and Implications

• Fairness and Robustness Trade-Offs: Through rigorous testing, Ditto is shown to reduce variance in test accuracy across devices by approximately 10% while enhancing average accuracy by 5% compared to fairness-specific state-of-the-art methods. These results underscore Ditto’s capacity to manage the fairness-robustness trade-off adeptly.
• Personalization Benefits: Compared to other personalization methods like Per-FedAvg and APFL, Ditto demonstrates competitive or superior performance. These outcomes highlight its efficacy in both homogeneous and heterogeneous federated settings, with or without adversaries.
• Future Prospects: The modular nature of Ditto suggests avenues for further development, including applications in privacy-preserving FL techniques and its potential extension to other adversarial models like backdoor attacks. Understanding Ditto's dynamics across more complex models or non-linear relationships remains an area ripe for exploration.

In conclusion, the Ditto framework furnishes a comprehensive approach to advancing fairness and robustness in federated learning. By allowing personalizations aligned closely with local data distributions, Ditto provides a nuanced alternative to singular global modelling, pushing the boundary of what federated systems can achieve under adversarial conditions and diverse data environments.