Defense Against Free-Rider Attack from the Weight Evolving Frequency

Federated learning (FL) with multiple clients collaborating to train a federated model without exchanging their individual data is a method of distributed machine learning. Although federated learning has gained an unprecedented success in data privacy preservation, its frailty of vulnerability to “free-rider” attacks attracts increasing attention. A number of defenses against free-rider attacks have been proposed for FL. Nevertheless, these methods may not protect against highly masquerading hitchhikers. Furthermore, when more than 20% of the clients are “hitchhikers”, the effectiveness of their defense may drop dramatically. To tackle these challenges, we reconceptualize the defense problem from a new perspective, i.e., the frequency of model weight evolution. Based on our experience, a new insight is gained that the frequency of model weight evolution is significantly different for free-riders and benign clients during the training process of FL. Motivated by this insight, a novel defense method based on the frequency of model weight evolution is proposed. In particular, a frequency of weight changes during the local training process is first collected. In the case of each client, it takes the WEF-Matrix of the local model and its weight of the model for each iteration and uploads it to the server. The server then separates “free-riders” from virtuous clients based on the difference in the WEF-Matrix. Finally, the broker uses a personalized method to offer different global models to the appropriate clients, thus keeping hitchhikers from obtaining high-value models. The combined experiments on five datasets and five models show that our method defends better than the state-of-the-art baseline and can identify hitchhikers at an early stage. The hitchhikers are identified at an early stage of training. Furthermore, we also verify the effectiveness of our method to adaptive attacks and visualize the WEF-Matrix during training to explain its effectiveness.