Explaining predictions and attacks in federated learning via random forests

Identifier:  imarina:9261618

Handle: https://hdl.handle.net/20.500.11797/imarina9261618

Authors:  Haffar, Rami; Sanchez, David; Domingo-Ferrer, Josep

Abstract:
Artificial intelligence (AI) is used for various purposes that are critical to human life. However, most state-of-the-art AI algorithms are black-box models, which means that humans cannot understand how such models make decisions. To forestall an algorithm-based authoritarian society, decisions based on machine learning ought to inspire trust by being explainable. For AI explainability to be practical, it must be feasible to obtain explanations systematically and automatically. A usual methodology to explain predictions made by a (black-box) deep learning model is to build a surrogate model based on a less difficult, more understandable decision algorithm. In this work, we focus on explaining by means of model surrogates the (mis)behavior of black-box models trained via federated learning. Federated learning is a decentralized machine learning technique that aggregates partial models trained by a set of peers on their own private data to obtain a global model. Due to its decentralized nature, federated learning offers some privacy protection to the participating peers. Nonetheless, it remains vulnerable to a variety of security attacks and even to sophisticated privacy attacks. To mitigate the effects of such attacks, we turn to the causes underlying misclassification by the federated model, which may indicate manipulations of the model. Our approach is to use random forests containing decision trees of restricted depth as surrogates of the federated black-box model. Then, we leverage decision trees in the forest to compute the importance of the features involved in the wrong predictions. We have applied our method to detect security and privacy attacks that malicious peers or the model manager may orchestrate in federated learning scenarios. Empirical results show that our method can detect attacks with high accuracy and, unlike other attack detection mechanisms, it can also explain the operation of such attacks at the peers’ side.