Improving Semi-supervised Federated Learning by Reducing the Gradient Diversity of Models. Abstract: Federated learning (FL) is a promising way to use the ...
Finally, we propose a grouping-based model averaging technique to replace FedAvg [2], to reduce gradient diversity further and to increase accuracy, especially.
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SSFL-Semi-supervised-Federated-Learning: Improving Semi-supervised Federated Learning by Reducing the Gradient Diversity of Models. Improving Semi-supervised ...
Motivated by these practical scenarios, we study the semi-supervised federated learning (SSFL) setting. In SSFL, users only have access to unlabeled data, while ...
Jan 3, 2024 · Federated Learning (FL) has emerged as a potent framework for training models across distributed data sources while maintaining data privacy.
Federated Learning The goal of Federated Learning is to scale and speed up the training of distributed models (Bonawitz et al., 2019; He et al., 2020).
Improving Semi-supervised Federated Learning by Reducing the Gradient Diversity of Models ; IEEE ICBD 2021.
A survey towards federated semi-supervised learning. arXiv ... Improving semi-supervised federated learning by reducing the gradient diversity of models.
This paper presents a robust semi-supervised FL system design, and proposes a novel aggregation rule based on the frequency of the client's participation in ...
Improving Semi-supervised Federated Learning by Reducing the Gradient Diversity of Models ... To run the main scripte "train_parallel.py", one needs to determine ...