Federated learning (FL) is designed to protect privacy of participants by not allowing direct
access to the participants' local datasets and training processes. This limitation hinders the …

Federated learning is a privacy-preserving distributed framework that facilitates information
fusion and sharing among different clients, enabling the training of a global model without …

Deep learning models used in medical image classification continue to be vulnerable to
adversarial attacks, particularly in the case of Invasive Ductal Carcinoma (IDC). The …

To mitigate the rising concern about privacy leakage, the federated recommender (FR)
paradigm emerges, in which decentralized clients co-train the recommendation model …

The rise of security concerns in conventional centralized learning has driven the adoption of
federated learning. However, the risks posed by poisoning attacks from internal adversaries …

Federated Learning (FL) is a privacy-focused revolutionary approach distributed paradigm
that supports considerable devices to train a shared model collaboratively without …

Federated learning (FL) is an emerging distributed machine learning paradigm that enables
participants to cooperatively train learning tasks without revealing the raw data. However …

論文抄録 分散データに対するプライバシーを保護した知識抽出手法として, 連合特異値分解が提案
されている. このフレームワークは, 分散データに対する特異値分解を可能にし …