Search Results for author: Hideya Ochiai

Found 9 papers, 4 papers with code

UniCon: Unidirectional Split Learning with Contrastive Loss for Visual Question Answering

no code implementations24 Aug 2022 Yuwei Sun, Hideya Ochiai

We propose the Unidirectional Split Learning with Contrastive Loss (UniCon) to tackle VQA tasks training on distributed data silos.

Contrastive Learning Question Answering +2

Semi-Targeted Model Poisoning Attack on Federated Learning via Backward Error Analysis

1 code implementation22 Mar 2022 Yuwei Sun, Hideya Ochiai, Jun Sakuma

To overcome this challenge, we propose the Attacking Distance-aware Attack (ADA) to enhance a poisoning attack by finding the optimized target class in the feature space.

Backdoor Attack Federated Learning +3

Feature Distribution Matching for Federated Domain Generalization

1 code implementation22 Mar 2022 Yuwei Sun, Ng Chong, Hideya Ochiai

In Federated Learning (FL), to leverage knowledge from different domains, learned model parameters are shared to train a global model.

Domain Generalization Federated Learning +5

Federated Phish Bowl: LSTM-Based Decentralized Phishing Email Detection

no code implementations12 Oct 2021 Yuwei Sun, Ng Chong, Hideya Ochiai

We collected the most recent phishing samples to study the effectiveness of the proposed method using different client numbers and data distributions.

Federated Learning Privacy Preserving

Homogeneous Learning: Self-Attention Decentralized Deep Learning

1 code implementation11 Oct 2021 Yuwei Sun, Hideya Ochiai

To this end, we propose a decentralized learning model called Homogeneous Learning (HL) for tackling non-IID data with a self-attention mechanism.

Image Classification Medical Image Classification +4

Information Stealing in Federated Learning Systems Based on Generative Adversarial Networks

no code implementations2 Aug 2021 Yuwei Sun, Ng Chong, Hideya Ochiai

At last, we successfully reconstructed the real data of the victim from the shared global model parameters with all the applied datasets.

Federated Learning

Decentralized Deep Learning for Multi-Access Edge Computing: A Survey on Communication Efficiency and Trustworthiness

no code implementations30 Jul 2021 Yuwei Sun, Hideya Ochiai, Hiroshi Esaki

Wider coverage and a better solution to a latency reduction in 5G necessitate its combination with multi-access edge computing (MEC) technology.

Distributed Computing Edge-computing +2

Intrusion Detection with Segmented Federated Learning for Large-Scale Multiple LANs

1 code implementation International Joint Conference on Neural Networks (IJCNN) 2020 Yuwei Sun, Hideya Ochiai, Hiroshi Esaki

In this research, a segmented federated learning is proposed, different from a collaborative learning based on single global model in a traditional federated learning model, it keeps multiple global models which allow each segment of participants to conduct collaborative learning separately and rearranges the segmentation of participants dynamically as well.

Network Intrusion Detection Personalized Federated Learning

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