Delta-DAGMM: A Free Rider Attack Detection Model in Horizontal Federated Learning

Abstract

Federated learning is a machine learning framework proposed in recent years. In horizontal federated learning, multiple participants cooperate to train and obtain a common final model. Participants only need to transmit the local updated model instead of local datasets. Some participants do not use effective local data sets, but provide disguised model parameters to participate in federal training and obtain common training models. This attack is called Free-rider attack. To the best of our knowledge, researches have proposed some Free-rider attack strategies with theoretical support, but there are few researches on Free-rider attack detection. However, the model disguised by some attackers using special attack strategies is similar to the real model in terms of convergence and weight, so it is difficult to detect the model provided by attacker as abnormal data. Based on DAGMM, a high-dimensional abnormal data detection model, this paper optimizes the sample processing and compression model, and proposes an improved detection algorithm, called Delta-DAGMM. Two types of large datasets are used for experiments. The experimental results show that Delta-DAGMM has higher precision and F1 score than DAGMM. On average, the Delta-DAGMM algorithm achieves a precision of 98.42% and an F1 score of 98.36%.