Fair Resource Allocation in Federated Learning (1905.10497v2)

Abstract: Federated learning involves training statistical models in massive, heterogeneous networks. Naively minimizing an aggregate loss function in such a network may disproportionately advantage or disadvantage some of the devices. In this work, we propose q-Fair Federated Learning (q-FFL), a novel optimization objective inspired by fair resource allocation in wireless networks that encourages a more fair (specifically, a more uniform) accuracy distribution across devices in federated networks. To solve q-FFL, we devise a communication-efficient method, q-FedAvg, that is suited to federated networks. We validate both the effectiveness of q-FFL and the efficiency of q-FedAvg on a suite of federated datasets with both convex and non-convex models, and show that q-FFL (along with q-FedAvg) outperforms existing baselines in terms of the resulting fairness, flexibility, and efficiency.

• The paper presents the q-FFL framework, reweighting device losses via a fairness parameter to achieve uniform model performance across heterogeneous devices.
• It extends FedAvg with dynamic step-size adaptation, reducing computational burden while accelerating convergence.
• Empirical validation shows up to a 45% reduction in variance, demonstrating improved fairness over traditional federated learning methods.

Fair Resource Allocation in Federated Learning

The paper "Fair Resource Allocation in Federated Learning" by Tian Li et al. introduces a novel approach to address fairness in federated learning (FL) environments. The authors propose $q$-Fair Federated Learning (FFL), an optimization problem designed to achieve a more uniform distribution of model performance across devices, inspired by fair resource allocation strategies from wireless networks.

Key Contributions

Federated learning traditionally aims to fit models using data across distributed networks without aggregating the data centrally. This inherently presents challenges related to data heterogeneity among devices, potentially leading to biased model performance that favors certain devices over others. The authors highlight this issue and propose FFL as an alternative to the standard aggregate loss minimization problem.

The $q$-FFL framework extends existing notions of fairness from network management, specifically drawing from the $\alpha$-fairness metric. The new objective reweights each device's contribution based on their loss, controlled by a fairness parameter, $q$. Larger $q$ values prioritize devices with higher losses, thereby mitigating performance disparities across devices. The method allows tuning of $q$ to balance overall performance and fairness, generalizing to the classical minimax fairness with sufficiently large $q$.

Methodology

To efficiently solve the $q$-FFL problem, the authors develop FedAvg, an extension of the FedAvg algorithm. It incorporates local updates within federated systems, leveraging a dynamic step-size strategy based on the Lipschitz constant, which can be estimated initially and adapted for different $q$ values. The dynamic adaptation avoids recomputation, easing computational burdens and accelerating convergence.

Additionally, the paper investigates the theoretical foundations of FFL, providing generalization bounds and demonstrating how increased $q$ can impose uniformity in performance measured via various fairness metrics. The authors further validate these theoretical findings with extensive experiments on both synthetic and real-world FL datasets, encompassing a range of convex and non-convex models.

Experimental Evaluation

The evaluation shows that FFL achieves significantly more uniform accuracy distributions among devices compared to traditional methods. Notably, their experiments indicate a 45% reduction in variance on average, using datasets like Sentiment140 and Shakespeare, while maintaining overall model accuracy.

Furthermore, the authors compare FFL with alternative fairness strategies, including uniform device weighting and adversarial approaches such as Agnostic Federated Learning (AFL). They observe that while AFL focuses on the worst-performing device, FFL offers a more flexible and efficient solution across more extensive networks, demonstrating improved fairness distribution and convergence speed.

Implications and Future Work

The proposed FFL framework highlights critical considerations of fairness in federated systems, particularly in applications requiring equitable performance across heterogeneous devices, such as IoT networks. The adaptable nature of the $q$ parameter positions FFL as a versatile tool, allowing users to tailor fairness according to application-specific needs.

The research opens avenues for expanding fairness concepts in machine learning beyond federated contexts. For instance, extending the approach to domains like meta-learning illustrates the framework's applicability in promoting fairness across diverse tasks without sacrificing average performance.

Future work could explore optimizing step-size estimation for diverse $q$ values and exploring more complex federated architectures. Additionally, real-world deployment of these concepts could produce insights into practical challenges and further refine the balance between fairness and performance in federated learning systems.

In conclusion, the paper presents a rigorous, theoretically grounded method for achieving fairness in federated learning, supported by robust empirical evidence, and offers a flexible toolset for managing fairness-performance trade-offs in distributed machine learning environments.