Data Poisoning Attacks in Gossip Learning - Sorbonne Université Access content directly
Reports (Technical Report) Year : 2024

Data Poisoning Attacks in Gossip Learning

Abstract

Traditional machine learning systems were designed in a centralized manner. In such designs, the central entity maintains both the machine learning model and the data used to adjust the model’s parameters. As data centralization yields privacy issues, Federated Learning was introduced to reduce data sharing and have a central server coordinate the learning of multiple devices. While Federated Learning is more decentralized, it still relies on a central entity that may fail or be subject to attacks, provoking the failure of the whole system. Then, Decentralized Federated Learning removes the need for a central server entirely, letting participating processes handle the coordination of the model construction. This distributed control urges studying the possibility of malicious attacks by the participants themselves. While poisoning attacks on Federated Learning have been extensively studied, their effects in Decentralized Federated Learning did not get the same level of attention. Our work is the first to propose a methodology to assess poisoning attacks in Decentralized Federated Learning in both churn free and churn prone scenarios. Furthermore, in order to evaluate our methodology on a case study representative for gossip learning we extended the gossipy simulator with an attack injector module.
Fichier principal
Vignette du fichier
main.pdf (1.43 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-04401682 , version 1 (19-02-2024)
hal-04401682 , version 2 (08-03-2024)

Identifiers

  • HAL Id : hal-04401682 , version 2

Cite

Alexandre Pham, Maria Potop-Butucaru, Sébastien Tixeuil, Serge Fdida. Data Poisoning Attacks in Gossip Learning. LIP6, Sorbonne Université, CNRS, UMR 7606. 2024. ⟨hal-04401682v2⟩
104 View
51 Download

Share

Gmail Facebook X LinkedIn More