Analyzing the vulnerabilities in Split Federated Learning: assessing the robustness against data poisoning attacks
Sign Up to like & getrecommendations! Published in 2025 at "Scientific Reports"
DOI: 10.1038/s41598-025-15993-8
Abstract: Distributed Collaborative Machine Learning (DCML) offers a promising alternative to address privacy concerns in centralized machine learning. Split learning (SL) and Federated Learning (FL) are two effective learning approaches within DCML. Recently, there has been… read more here.
Keywords: data poisoning; split federated; attack; federated learning ... See more keywords
A Data Poisoning Resistible and Privacy Protection Federated-Learning Mechanism for Ubiquitous IoT
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Internet of Things Journal"
DOI: 10.1109/jiot.2024.3514637
Abstract: As a novel distributed learning paradigm, federated learning (FL) allows clients to train global models collaboratively without exchanging private data. However, recent research not only demonstrates the vulnerability of FL against privacy attacks where adversaries… read more here.
Keywords: data poisoning; privacy; resistible privacy; federated learning ... See more keywords
Data Poisoning Attacks in Internet-of-Vehicle Networks: Taxonomy, State-of-The-Art, and Future Directions
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Industrial Informatics"
DOI: 10.1109/tii.2022.3198481
Abstract: With the unprecedented development of deep learning, autonomous vehicles (AVs) have achieved tremendous progress nowadays. However, AV supported by DNN models is vulnerable to data poisoning attacks, hindering the large-scale application of autonomous driving. For… read more here.
Keywords: attacks defenses; data poisoning; state art; poisoning attacks ... See more keywords
LOKI: A Practical Data Poisoning Attack Framework Against Next Item Recommendations
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Knowledge and Data Engineering"
DOI: 10.1109/tkde.2022.3181270
Abstract: Due to the openness of the online platform, recommendation systems are vulnerable to data poisoning attacks, where malicious samples are injected into the training set of the recommendation system to manipulate its recommendation results. Existing… read more here.
Keywords: system; recommendation; recommendation systems; poisoning attack ... See more keywords
Influence-Driven Data Poisoning for Robust Recommender Systems.
Sign Up to like & getrecommendations! Published in 2023 at "IEEE transactions on pattern analysis and machine intelligence"
DOI: 10.1109/tpami.2023.3274759
Abstract: Recent studies have shown that recommender systems are vulnerable, and it is easy for attackers to inject well-designed malicious profiles into the system, resulting in biased recommendations. We cannot deprive these data's injection right and… read more here.
Keywords: influence; driven data; recommender systems; data poisoning ... See more keywords
Data Poisoning Attack on Black-Box Neural Machine Translation to Truncate Translation
Sign Up to like & getrecommendations! Published in 2024 at "Entropy"
DOI: 10.3390/e26121081
Abstract: Neural machine translation (NMT) systems have achieved outstanding performance and have been widely deployed in the real world. However, the undertranslation problem caused by the distribution of high-translation-entropy words in source sentences still exists, and… read more here.
Keywords: data poisoning; attack; machine translation; neural machine ... See more keywords
TPoison: Data-Poisoning Attack against GNN-Based Social Trust Model
Sign Up to like & getrecommendations! Published in 2024 at "Mathematics"
DOI: 10.3390/math12121813
Abstract: In online social networks, users can vote on different trust levels for each other to indicate how much they trust their friends. Researchers have improved their ability to predict social trust relationships through a variety… read more here.
Keywords: data poisoning; gnn; attack; model ... See more keywords