Quantization in Federated Learning: Methods, Challenges and Future Directions

2026-06-25

AI Summary

Federated learning faces significant scalability challenges due to communication bottlenecks, device heterogeneity, and non-independent and identically distributed (non-IID) data. This work presents the first comprehensive taxonomy of quantization methods centered on federated learning, systematically reviewing existing approaches through six key dimensions: client heterogeneity, aggregation consistency, communication scheduling adaptation, robustness to non-IID data, integration with privacy and security mechanisms, and hardware-energy efficiency co-design. Through an in-depth literature review and cross-dimensional analysis, the study elucidates how quantization influences core federated mechanisms—such as client drift, partial participation, and convergence stability—and distills general design principles. It establishes quantization as a foundational system component in federated learning, identifies critical research gaps, and offers practical guidelines for efficient deployment in mobile, IoT, and edge computing scenarios.

Abstract

Federated Learning (FL) has become a foundational paradigm for privacy-preserving distributed intelligence, yet its scalability remains fundamentally constrained by communication bottlenecks, device heterogeneity, and the challenges of training under statistically non-IID data. Quantization is one of the most effective mechanisms for mitigating these limitations, reducing both uplink/downlink payloads and on-device computation. This paper provides the first FL-centric systematic review of quantization, introducing a novel taxonomy organized around FL-specific dimensions, including client heterogeneity, aggregation consistency, communication-scheduling adaptation, non-IID robustness, privacy/security integration, and hardware/energy co-optimization. Beyond cataloging existing methods, we analyze how quantization interacts with core FL behaviors such as client drift, partial participation, convergence stability, secure aggregation, and differential privacy. We further identify cross-method insights, open research gaps, and design guidelines for practitioners deploying quantized FL on mobile, IoT, and edge platforms. This survey thus establishes quantization not merely as a compression technique, but as a fundamental systems component shaping the performance, robustness, and practicality of modern FL.

Problem

• Federated Learning
• Quantization
• Communication Bottleneck
• Device Heterogeneity
• Non-IID Data

Innovation

• quantization
• federated learning
• client heterogeneity
• non-IID robustness
• communication efficiency