Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challenges
By Nuria Rodr'iguez-Barroso, Daniel Jim'enez L'opez, María Victoria Luzón, Francisco Herrera, Eugenio Martínez-Cámara. Information Fusion, 2022
318 citations recorded by the source corpus.
Federated learning is a machine learning paradigm that emerges as a solution to the privacy-preservation demands in artificial intelligence. As machine learning, federated learning is threatened by adversarial attacks against the integrity of the learning model and the privacy of data via a distributed approach to tackle local and global learning. This weak point is exacerbated by the inaccessibility of data in federated learning, which makes harder the protection against adversarial attacks and evidences the need to furtherance the research on defence methods to make federated learning a real solution for safeguarding data privacy. In this paper, we present an extensive review of the threats of federated learning, as well as as their corresponding countermeasures, attacks versus defences. This survey provides a taxonomy of adversarial attacks and a taxonomy of defence methods that depict a general picture of this vulnerability of federated learning and how to overcome it. Likewise, we expound guidelines for selecting the most adequate defence method according to the category of the adversarial attack. Besides, we carry out an extensive experimental study from which we draw further conclusions about the behaviour of attacks and defences and the guidelines for selecting the most adequate defence method according to the category of the adversarial attack. This study is finished leading to meditated learned lessons and challenges.
Subfields: Artificial Intelligence, Health Informatics
View Paper: https://www.semanticscholar.org/paper/0ed8340d8c7e46047677c84929e72b3f1fc274c2
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Citations in the corpus
- A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications (2023, background): “• Federated learning is a DL technique that allows groups or organizations to collectively train and improve a shared global DL model [138].”
- Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions (2023, background): “These nodes typically train models on their local datasets and periodically share information with other nodes to improve a global model [139].”
- Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges (2022, background): “the attack (training or evaluation time) and frequency (one-shot or multiple) [124].”