Security of Federated Learning: Attacks, Defensive Mechanisms, and Challenges - Archive ouverte HAL
Article Dans Une Revue Revue des Sciences et Technologies de l'Information - Série RIA : Revue d'Intelligence Artificielle Année : 2022

Security of Federated Learning: Attacks, Defensive Mechanisms, and Challenges

Résumé

Recently, a new Artificial Intelligence (AI) paradigm, known as Federated Learning (FL), has been introduced. It is a decentralized approach to apply Machine Learning (ML) ondevice without risking the disclosure and tracing of sensitive and private information. Instead of training the global model on a centralized server (by aggregating the clients' private data), FL trains a global shared model by only aggregating clients' locally-computed updates (the clients' private data remains distributed across the clients' devices). However, as secure as the FL seems, it by itself does not give the levels of privacy and security required by today's distributed systems. This paper seeks to provide a holistic view of FL's security concerns. We outline the most important attacks and vulnerabilities that are highly relevant to FL systems. Then, we present the recent proposed defensive mechanisms. Finally, we highlight the outstanding challenges, and we discuss the possible future research directions.
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Dates et versions

hal-03620400 , version 1 (25-03-2022)

Identifiants

Citer

Mourad Benmalek, Mohamed Ali Benrekia, Yacine Challal. Security of Federated Learning: Attacks, Defensive Mechanisms, and Challenges. Revue des Sciences et Technologies de l'Information - Série RIA : Revue d'Intelligence Artificielle, 2022, 36 (1), pp.49-59. ⟨10.18280/ria.360106⟩. ⟨hal-03620400⟩

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