arXiv:2606.26822v1 Announce Type: new 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 aroun
The proliferation of distributed AI applications and the increasing scale of models are spotlighting communication bottlenecks, making efficient data transfer critical.
This paper addresses a fundamental challenge in scaling federated learning, which is crucial for privacy-preserving and distributed AI development across diverse devices.
Improved quantization techniques will enable more efficient federated learning deployments, potentially broadening the applicability and performance of distributed AI systems.
- · Edge AI device manufacturers
- · Companies deploying federated learning
- · AI researchers
- · Less efficient distributed computing paradigms
More robust and scalable federated learning systems become feasible.
Increased adoption of privacy-preserving AI across various industries, from healthcare to finance.
Accelerated development of AI applications on resource-constrained devices, fostering new user experiences and data collection methods.
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