Quantization in Federated Learning: Methods, Challenges and Future Directions

• URL: http://arxiv.org/abs/2606.26822v1
• Date: Thu, 25 Jun 2026 10:03:55 GMT
• Title: Quantization in Federated Learning: Methods, Challenges and Future Directions
• Abstract summary: This paper provides the first FL-centric systematic review of quantization, introducing a novel taxonomy organized around FL-specific dimensions.<n>We analyze how quantization interacts with core FL behaviors such as client drift, partial participation, convergence stability, secure aggregation, and differential privacy.
• Score: 6.698585272555488
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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