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Federated learning (FL) has recently emerged as a new form of collaborativemachine learning, where a common model can be learned while keeping all thetraining data on local devices. Although it is designed for enhancing the dataprivacy, we demonstrated in this paper a new direction in inference attacks inthe context of FL, where valuable information about training data can beobtained by adversaries with very limited power. In particular, we proposedthree new types of attacks to exploit this vulnerability. The first type ofattack, Class Sniffing, can detect whether a certain label appears in training.The other two types of attacks can determine the quantity of each label, i.e.,Quantity Inference attack determines the composition proportion of the traininglabel owned by the selected clients in a single round, while WholeDetermination attack determines that of the whole training process. Weevaluated our attacks on a variety of tasks and datasets with differentsettings, and the corresponding results showed that our attacks work wellgenerally. Finally, we analyzed the impact of major hyper-parameters to ourattacks and discussed possible defenses.

Eavesdrop the Composition Proportion of Training Labels in Federated Learning