Search Results for author: Yuwei Sun

Found 15 papers, 6 papers with code

Associative Transformer

1 code implementation22 Sep 2023 Yuwei Sun, Hideya Ochiai, Zhirong Wu, Stephen Lin, Ryota Kanai

Existing studies such as the Coordination method employ iterative cross-attention mechanisms with a bottleneck to enable the sparse association of inputs.

Artificial Global Workspace Inductive Bias +2

Meta Neural Coordination

no code implementations20 May 2023 Yuwei Sun

This requires a model of how other learning algorithms operate and perform in different contexts, which is similar to representing and reasoning about mental states in the theory of mind.

Meta-Learning

Instance-Level Trojan Attacks on Visual Question Answering via Adversarial Learning in Neuron Activation Space

no code implementations2 Apr 2023 Yuwei Sun, Hideya Ochiai, Jun Sakuma

To this end, we propose an instance-level multimodal Trojan attack on VQA that efficiently adapts to fine-tuned models through a dual-modality adversarial learning method.

Question Answering Visual Question Answering

Meta Learning in Decentralized Neural Networks: Towards More General AI

no code implementations2 Feb 2023 Yuwei Sun

Meta-learning usually refers to a learning algorithm that learns from other learning algorithms.

Meta-Learning

Resilience of Wireless Ad Hoc Federated Learning against Model Poisoning Attacks

no code implementations7 Nov 2022 Naoya Tezuka, Hideya Ochiai, Yuwei Sun, Hiroshi Esaki

Compared to conventional federated learning, WAFL performs model training by weakly synchronizing the model parameters with others, and this shows great resilience to a poisoned model injected by an attacker.

Federated Learning Model Poisoning

Bidirectional Contrastive Split Learning for Visual Question Answering

no code implementations24 Aug 2022 Yuwei Sun, Hideya Ochiai

To this end, we propose Bidirectional Contrastive Split Learning (BiCSL) to train a global multi-modal model on the entire data distribution of decentralized clients.

Backdoor Attack Contrastive Learning +4

Wireless Ad Hoc Federated Learning: A Fully Distributed Cooperative Machine Learning

no code implementations24 May 2022 Hideya Ochiai, Yuwei Sun, Qingzhe Jin, Nattanon Wongwiwatchai, Hiroshi Esaki

WAFL can develop generalized models from Non-IID datasets stored in distributed nodes locally by exchanging and aggregating them with each other over opportunistic node-to-node contacts.

Autonomous Vehicles BIG-bench Machine Learning +1

Feature Distribution Matching for Federated Domain Generalization

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

The empirical results show that FedKA achieves performance gains of 8. 8% and 3. 5% in Digit-Five and Office-Caltech10, respectively, and a gain of 0. 7% in Amazon Review with extremely limited training data.

Domain Generalization Federated Learning +6

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

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

Adaptive Intrusion Detection in the Networking of Large-Scale LANs with Segmented Federated Learning

1 code implementation IEEE Open Journal of the Communications Society (Conference version: IJCNN) 2020 Yuwei Sun, Hiroshi Esaki, Hideya Ochiai.

We propose Segmented-Federated Learning (Segmented-FL), where by employing periodic local model evaluation and network segmentation, we aim to bring similar network environments to the same group.

Network Intrusion Detection Personalized Federated Learning

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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