Federated Learning with Fair Averaging (2104.14937v5)

Abstract: Fairness has emerged as a critical problem in federated learning (FL). In this work, we identify a cause of unfairness in FL -- conflicting gradients with large differences in the magnitudes. To address this issue, we propose the federated fair averaging (FedFV) algorithm to mitigate potential conflicts among clients before averaging their gradients. We first use the cosine similarity to detect gradient conflicts, and then iteratively eliminate such conflicts by modifying both the direction and the magnitude of the gradients. We further show the theoretical foundation of FedFV to mitigate the issue conflicting gradients and converge to Pareto stationary solutions. Extensive experiments on a suite of federated datasets confirm that FedFV compares favorably against state-of-the-art methods in terms of fairness, accuracy and efficiency. The source code is available at https://github.com/WwZzz/easyFL.

• The paper introduces FedFV, a novel algorithm that mitigates gradient conflicts to enhance fairness among federated learning clients.
• The methodology uses cosine similarity to adjust the direction and magnitude of client gradients, preventing skewed updates.
• Empirical results show up to a 7.2% accuracy improvement on CIFAR-10 with reduced variance, demonstrating enhanced model uniformity.

Federated Learning with Fair Averaging: A Technical Overview

The paper "Federated Learning with Fair Averaging" by Zheng Wang et al. addresses a critical challenge in federated learning (FL): the issue of fairness among participating clients. Federated Learning is a distributed machine learning approach that enables model training across multiple clients without sharing raw data. This paradigm helps preserve data privacy but often leads to unfairness in the global model's performance across diverse datasets distributed among clients.

Key Contributions

The authors identify a prevailing cause of unfairness in FL: conflicting gradients with large magnitude differences. In response, they propose a novel algorithm, Federated Fair Averaging (FedFV), designed to mitigate these gradient conflicts before aggregating them. The key contributions of this work are:

1. Identification of Gradient Conflicts: The authors distinguish two types of conflicts — internal (among currently selected clients) and external (between selected and non-selected clients). These conflicts can skew the aggregate gradient during federated updates, leading to suboptimal performance for some clients.
2. FedFV Algorithm: The proposed method uses cosine similarity to detect conflicting gradients. It iteratively resolves conflicts by adjusting both the direction and magnitude of client gradients. This adjustment, based on a specific projection order rooted in theoretical analysis, leads to a more balanced gradient averaging process.
3. Theoretical Foundation and Convergence: The paper provides a theoretical framework demonstrating how FedFV successfully mitigates gradient conflicts and achieves convergence to Pareto stationary solutions or the optimal on convex problems.
4. Empirical Evaluation: Extensive experiments show that FedFV outperforms state-of-the-art methods regarding fairness, accuracy, and efficiency across a range of federated datasets.

Methodology Discussion

FedFV focuses on addressing the imbalance caused by heterogeneous and imbalanced data distributions across clients. The algorithm consists of two main procedures:

• Mitigating Internal Conflicts: By projecting gradients onto non-conflicting subspaces, FedFV minimizes internal conflicts within each communication round. This ensures that the skewness induced by larger, more confidently oriented gradients does not overwhelmingly dictate the direction of the aggregate update.
• Mitigating External Conflicts: This aspect considers the fairness over multiple rounds, accounting for clients that are intermittently unavailable or not selected, thereby preventing the bias introduced by selective client participation.

The paper rigorously proves the convergence of FedFV to optimal or near-optimal solutions, leveraging smoothness assumptions typically held in machine learning literature.

Numerical Results

Experiments conducted on datasets such as CIFAR-10, Fashion MNIST, and MNIST demonstrate the ability of FedFV to significantly reduce performance variance across clients. For instance, FedFV achieves up to 7.2% improvement in mean accuracy on CIFAR-10 while maintaining low variance, indicating more uniform model performance across different client datasets.

Implications and Future Work

The implementation of FedFV can enhance the performance of FL systems when applied to domains requiring fairness, such as finance, healthcare, and personalized services, where client data distributions are inherently non-IID and unbalanced. Theoretical advancements in balancing gradient magnitudes can lead to more robust systems capable of addressing further complexities in FL environments.

Future developments might explore a complete theoretical analysis of FedFV concerning external conflicts and broader client participation scenarios. Further work could also investigate adaptive mechanisms to dynamically adjust projection sensitivity based on real-time data characteristics, enhancing the robustness and adaptability of federated learning systems.