17 citations · 45 across the 6 of their papers we have counts for
6 papers
Federated Source-free Domain Adaptation for Classification: Weighted Cluster Aggregation for Unlabeled Data
Junki Mori, Kosuke Kihara, Taiki Miyagawa +3
Federated learning (FL) commonly assumes that the server or some clients have labeled data, which is often impractical due to annotation costs and privacy concerns. Addressing this…
Survey of Privacy Threats and Countermeasures in Federated Learning
Masahiro Hayashitani, Junki Mori, Isamu Teranishi
Federated learning is widely considered to be as a privacy-aware learning method because no training data is exchanged directly between clients. Nevertheless, there are threats to…
Heterogeneous Domain Adaptation with Positive and Unlabeled Data
Junki Mori, Ryo Furukawa, Isamu Teranishi +1
Heterogeneous unsupervised domain adaptation (HUDA) is the most challenging domain adaptation setting where the feature spaces of source and target domains are heterogeneous, and t…
Personalized Federated Learning with Multi-branch Architecture
Junki Mori, Tomoyuki Yoshiyama, Furukawa Ryo +1
Federated learning (FL) is a decentralized machine learning technique that enables multiple clients to collaboratively train models without requiring clients to reveal their raw da…
Continual Horizontal Federated Learning for Heterogeneous Data
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. Among vari…
Knowledge Cross-Distillation for Membership Privacy
Rishav Chourasia, Batnyam Enkhtaivan, Kunihiro Ito +3
A membership inference attack (MIA) poses privacy risks for the training data of a machine learning model. With an MIA, an attacker guesses if the target data are a member of the t…