BOBA: Byzantine-Robust Federated Learning with Label Skewness

Core Concepts

BOBA provides unbiased and robust aggregation in federated learning, addressing label skewness challenges.

Abstract

The content discusses the challenges of label skewness in federated learning and introduces BOBA as a solution. It covers the theoretical analysis, algorithm stages, computational complexity, and experimental evaluations on various datasets and attacks. Introduction Federated learning (FL) system overview. Challenges of Byzantine attacks in FL. Label Skewness Analysis Definition of label skew distribution. Honest gradients distribution analysis. Challenges of Label Skewness Selection bias and increased vulnerability explained. Proposed BOBA Algorithm Two-stage method: fitting honest subspace and finding honest simplex. Theoretical Analysis Connection between convergence and gradient estimation error. Experiments Evaluation of unbiasedness, robustness, efficiency, effect of server data, hyper-parameters, label skewness settings. Evaluation Results Unbiasedness evaluation shows BOBA's superior performance compared to baseline AGRs. Robustness evaluation demonstrates BOBA's effectiveness against various attacks. Further Questions

Stats

In this paper, we address label skewness in federated learning. We propose an efficient two-stage method named BOBA with proven convergence guarantees.

Quotes

"We introduce BOBA to tackle the limitations of existing AGRs." "BOBA demonstrates superior unbiasedness and robustness across diverse models."

Key Insights Distilled From

BOBA

by Wenxuan Bao,... at arxiv.org 03-21-2024

https://arxiv.org/pdf/2208.12932.pdf

Deeper Inquiries

How does the quality of server data impact the performance of BOBA

The quality of server data plays a crucial role in determining the performance of BOBA in federated learning. High-quality server data, free from noise or corruption, enables BOBA to make accurate estimations and decisions during the aggregation process. With clean and reliable server data, BOBA can effectively identify honest gradients, mitigate bias introduced by Byzantine clients, and ensure robustness against attacks. On the other hand, low-quality server data with noise or inaccuracies can hinder BOBA's ability to differentiate between honest and malicious gradients. This may lead to incorrect estimations, increased vulnerability to attacks, and compromised model convergence.

What are the implications of selection bias in federated learning systems

Selection bias in federated learning systems refers to the tendency of certain aggregation rules to favor specific clients over others during the model training process. This bias arises when certain clients' contributions are disproportionately weighted or prioritized over others based on their characteristics or performance metrics. In practical terms, selection bias can lead to skewed model updates that favor particular classes or patterns present in a subset of client data while neglecting others. This imbalance can result in suboptimal model performance for underrepresented classes or datasets. Implications: Unfair Model Training: Selection bias can lead to unfairness in model training by giving preferential treatment to specific clients or types of data. Reduced Generalization: Biased aggregation rules may limit the generalizability of models trained on federated learning systems by focusing on only a subset of available information. Vulnerability to Attacks: Selection bias can make federated learning systems more susceptible to adversarial attacks as attackers exploit biased aggregation mechanisms for malicious purposes. Inefficient Resource Allocation: Biased aggregation may result in inefficient resource allocation within the system, leading to suboptimal utilization of client contributions.

How can the concept of label skewness be applied to other machine learning scenarios

The concept of label skewness observed in federated learning scenarios where each client has access only to a few classes of data can be applied across various machine learning contexts beyond FL: 1- Imbalanced Datasets: Label skewness is akin to imbalanced datasets where some classes have significantly fewer samples than others. By understanding label distribution variations among different subsets within an imbalanced dataset, ML models could be optimized for better class representation and prediction accuracy. 2- Multi-Task Learning: In multi-task learning setups where different tasks have varying levels of importance or prevalence, considering label skewness helps prioritize tasks based on their relevance and impact on overall model performance. 3- Transfer Learning: When transferring knowledge from one domain/task with distinct class distributions (label skew)to another related domain/task with similar patterns but different labels distribution , acknowledging label skewness aids effective transfer strategy design . By incorporating insights from label skewness into these scenarios , we enhance our understanding about how class distributions affect modeling outcomes . It allows us optimize algorithms for improved predictions across diverse applications .