Nov 6, 2020 · In this paper, we propose a novel federated split learning framework, FedSL, to train models on distributed sequential data.

Sep 8, 2023 · In this paper, we propose a novel federated split learning framework, FedSL, to train models on distributed sequential data.

The experimental results on simulated and real-world datasets demonstrate that the proposed method successfully train models on distributed sequential data, ...

In this paper, we propose a novel federated split learning framework, FedSL, to train models on distributed sequential data. The most common ML models to train ...

Implementation of FedSL: Federated Split Learning on Distributed Sequential Data in Recurrent Neural Networks. 7 stars 2 forks Branches Tags Activity.

Sep 8, 2023 · In this paper, we propose a novel federated split learning framework, FedSL, to train models on distributed sequential data. The most common ML ...

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This study incorporates federated learning and split learning paradigms with STINs and introduces a split-then-federated learning framework and federated split ...

A curated repository for various papers in the domain of split learning. Split Training. Split learning for health: Distributed deep learning without sharing ...

Oct 20, 2022 · Split learning is an emerging distributed machine learning paradigm that exhibits great advantages in privacy protection and training ...