Oct 28, 2024 · This work proposes a novel one-shot FL framework by embodying each local model as an independent expert and leveraging a Mixture-of-Experts network to maintain ...
Aug 24, 2024 · This paper studies the one-shot federated learning problem where the communication between server and clients is only conducted one time.
Nov 1, 2024 · Abstract. Transforming the multi-round vanilla Federated Learning (FL) into one-shot FL (OFL) significantly reduces the communication burden.
Nov 16, 2024 · One-shot-but-not-degraded Federated Learning. October 2024. DOI ... Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boosting.
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This work presents one-shot federated learning, where a central server learns a global model over a network of federated devices in a single round of ...
6 days ago · We demonstrate that Task Arithmetic is mathematically equivalent to the commonly used algorithm in Federated Learning, called Federated ...
Feb 2, 2024 · In one-shot federated learning (FL), clients collaboratively train a global model in a single round of communication.
Abstract. In this work, we explore the unique challenges— and opportunities—of unsupervised federated learning (FL). We develop and analyze a one-shot.
A novel two-stage DENSE framework, which trains the global model by a data generation stage and a model distillation stage.
Oct 21, 2024 · The FedLPA method proposed in this paper offers a promising solution for one-shot federated learning in the presence of non-identical data distributions.