Exploiting Correlations in Federated Learning: Opportunities and Practical Limitations

2026-04-16

AI Summary

This work addresses the communication bottleneck in federated learning by proposing a unified compression framework grounded in correlation analysis. The framework systematically categorizes gradient and model compression techniques according to the type of correlation they exploit—structural, temporal, or spatial—and introduces quantitative metrics to measure the strength of each correlation type. Building on this taxonomy, an adaptive compression mechanism is designed to dynamically select the most effective compression strategy based on real-time correlation strengths. Experimental results across diverse tasks, models, and algorithmic configurations reveal significant variations in the strength of the three correlation types, demonstrating that the proposed adaptive approach consistently outperforms existing non-adaptive compression methods in communication efficiency. This study establishes the first correlation-based classification system tailored specifically for compression techniques in federated learning.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Data Mining & Knowledge Management: Data Compression
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

The communication bottleneck in federated learning (FL) has spurred extensive research into techniques to reduce the volume of data exchanged between client devices and the central parameter server. In this paper, we systematically classify gradient and model compression schemes into three categories based on the type of correlations they exploit: structural, temporal, and spatial. We examine the sources of such correlations, propose quantitative metrics for measuring their magnitude, and reinterpret existing compression methods through this unified correlation-based framework. Our experimental studies demonstrate that the degrees of structural, temporal, and spatial correlations vary significantly depending on task complexity, model architecture, and algorithmic configurations. These findings suggest that algorithm designers should carefully evaluate correlation assumptions under specific deployment scenarios rather than assuming that they are always present. Motivated by these findings, we propose two adaptive compression designs that actively switch between different compression modes based on the measured correlation strength, and we evaluate their performance gains relative to conventional non-adaptive approaches. In summary, our unified taxonomy provides a clean and principled foundation for developing more effective and application-specific compression techniques for FL systems.

Problem

• federated learning
• communication bottleneck
• gradient compression
• model compression
• correlation

Innovation

• correlation-aware compression
• federated learning
• adaptive compression
• gradient sparsity
• communication efficiency