Towards One-shot Federated Learning: Advances, Challenges, and Future Directions

TL;DR

This paper surveys the state of one-shot federated learning, emphasizing its potential for resource-constrained environments, analyzing current methods, challenges, and future directions for scalable, generalizable solutions.

Contribution

It provides a comprehensive categorization of existing one-shot FL methods, discusses key challenges, and outlines future research directions for practical deployment.

Findings

One-shot FL enables single-round model aggregation for resource-limited devices.

Current approaches face challenges in scalability and handling non-IID data.

The survey identifies open problems and potential solutions for future research.

Abstract

One-shot FL enables collaborative training in a single round, eliminating the need for iterative communication, making it particularly suitable for use in resource-constrained and privacy-sensitive applications. This survey offers a thorough examination of One-shot FL, highlighting its distinct operational framework compared to traditional federated approaches. One-shot FL supports resource-limited devices by enabling single-round model aggregation while maintaining data locality. The survey systematically categorizes existing methodologies, emphasizing advancements in client model initialization, aggregation techniques, and strategies for managing heterogeneous data distributions. Furthermore, we analyze the limitations of current approaches, particularly in terms of scalability and generalization in non-IID settings. By analyzing cutting-edge techniques and outlining open challenges, this survey aspires to provide a comprehensive reference for researchers and practitioners aiming to design and implement One-shot FL systems, advancing the development and adoption of One-shot FL solutions in a real-world, resource-constrained scenario.