Towards One-shot Federated Learning: Advances, Challenges, and Future Directions
It provides a comprehensive reference for researchers and practitioners working on federated learning in resource-limited and privacy-sensitive settings, but it is incremental as a survey.
This survey examines one-shot federated learning, which enables collaborative training in a single round to reduce communication overhead, and analyzes its methodologies, limitations, and future directions for resource-constrained applications.
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.