Understanding Federated Learning from IID to Non-IID dataset: An Experimental Study

2025-01-31

AI Summary

Federated learning (FL) suffers significant performance degradation under non-independent and identically distributed (Non-IID) data, primarily due to inconsistent loss landscapes across clients causing conflicting gradient updates. Method: This work establishes the first unified analytical framework covering both IID and Non-IID settings, grounded in the fundamental principles of gradient descent. It introduces the novel concept of “loss landscape consistency” and systematically categorizes existing FL methods into two paradigms: parameter update path adjustment and loss landscape correction. Using distributed optimization theory, loss landscape visualization, and extensive simulations across heterogeneous client configurations, the study quantitatively validates strong correlations between landscape divergence and convergence behavior/accuracy. Contribution/Results: The framework provides interpretable design principles for Non-IID FL algorithms, enabling principled mitigation of statistical heterogeneity and substantially improving cross-distribution generalization robustness.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Search and Optimization: Non-convex Optimization
• Natural Language Processing: Learning & Optimization for NLP

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

As privacy concerns and data regulations grow, federated learning (FL) has emerged as a promising approach for training machine learning models across decentralized data sources without sharing raw data. However, a significant challenge in FL is that client data are often non-IID (non-independent and identically distributed), leading to reduced performance compared to centralized learning. While many methods have been proposed to address this issue, their underlying mechanisms are often viewed from different perspectives. Through a comprehensive investigation from gradient descent to FL, and from IID to non-IID data settings, we find that inconsistencies in client loss landscapes primarily cause performance degradation in non-IID scenarios. From this understanding, we observe that existing methods can be grouped into two main strategies: (i) adjusting parameter update paths and (ii) modifying client loss landscapes. These findings offer a clear perspective on addressing non-IID challenges in FL and help guide future research in the field.

Problem

• Federated Learning
• Non-IID Data
• Performance Degradation

Innovation

• Federated Learning
• Data Distribution
• Learning Strategy Adjustment