no code implementations • 1 Feb 2024 • Masahiro Hayashitani, Junki Mori, Isamu Teranishi
In this paper, we describe privacy threats and countermeasures for the typical types of federated learning; horizontal federated learning, vertical federated learning, and transfer federated learning.
no code implementations • 17 Apr 2023 • Junki Mori, Ryo Furukawa, Isamu Teranishi, Jun Sakuma
To overcome this issue, we propose a novel method, predictive adversarial domain adaptation (PADA), which can predict likely positive examples from the unlabeled target data and simultaneously align the feature spaces to reduce the distribution divergence between the whole source data and the likely positive target data.
no code implementations • 15 Nov 2022 • Junki Mori, Tomoyuki Yoshiyama, Furukawa Ryo, Isamu Teranishi
We also design an aggregation method to improve the communication efficiency and the model performance, with which each branch is globally updated with weighted averaging by client-specific weights assigned to the branch.
no code implementations • 4 Mar 2022 • Junki Mori, Isamu Teranishi, Ryo Furukawa
Federated learning is a promising machine learning technique that enables multiple clients to collaboratively build a model without revealing the raw data to each other.
no code implementations • 2 Nov 2021 • Rishav Chourasia, Batnyam Enkhtaivan, Kunihiro Ito, Junki Mori, Isamu Teranishi, Hikaru Tsuchida
A membership inference attack (MIA) poses privacy risks for the training data of a machine learning model.