User‐Level Membership Inference for Federated Learning in Wireless Network Environment

Abstract

With the rise of privacy concerns in traditional centralized machine learning services, federated learning, which incorporates multiple participants to train a global model across their localized training data, has lately received significant attention in both industry and academia. Bringing federated learning into a wireless network scenario is a great move. The combination of them inspires tremendous power and spawns a number of promising applications. Recent researches reveal the inherent vulnerabilities of the various learning modes for the membership inference attacks that the adversary could infer whether a given data record belongs to the model’s training set. Although the state-of-the-art techniques could successfully deduce the membership information from the centralized machine learning models, it is still challenging to infer the member data at a more confined level, the user level. It is exciting that the common wireless monitor technique in the wireless network environment just provides a good ground for fine-grained membership inference. In this paper, we novelly propose and define a concept of user-level inference attack in federated learning. Specifically, we first give a comprehensive analysis of active and targeted membership inference attacks in the context of federated learning. Then, by considering a more complicated scenario that the adversary can only passively observe the updating models from different iterations, we incorporate the generative adversarial networks into our method, which can enrich the training set for the final membership inference model. In the end, we comprehensively research and implement inferences launched by adversaries of different roles, which makes the attack scenario complete and realistic. The extensive experimental results demonstrate the effectiveness of our proposed attacking approach in the case of single label and multilabel.

Interaction-level Membership Inference Attack Against Federated Recommender Systems

“…Then, many works took further steps to study more fine-grained membership inference attacks, e.g. [12,26,30,36,49]. However, existing membership inference attacks cannot be used in FedRec because of the major differences mentioned in Section 1.…”

“…Inferring a user's interaction data in FedRecs is one type of membership inference attack (MIA). Although MIA has been widely investigated in federated classification tasks [4,21,25,30,43,49], their proposed attack and defense approaches cannot apply to Fe-dRecs due to the following major differences between federated recommendation and federated classification. (1) From the perspective of attack objective, MIA in federated classification aims to infer or predict whether a sample has been used in the federated training process and which client has used it for the local training.…”