How to cope with malicious federated learning clients: An unsupervised learning-based approach

Abstract

With the advent of data-driven and Artificial Intelligence solutions, data providers such as Internet of Things (IoT)-enabled devices and sensors have become essential for intelligent services. In conventional machine learning, data collected from sensors and edge devices are transmitted to a centralized server for model training. Although it achieves state-of-the-art results, this process has drawbacks, such as privacy concerns, data leakage, etc. Specifically, some companies or services like hospitals want to keep their data themselves. Since they do not share data on a server, training on the centralized server cannot take the overall benefit of all data. Therefore a new training method is proposed, namely federated learning. In this learning, users keep their data to themselves and train their local model with their data. After training, users send their model weights to the server instead of data. When the centralized server takes all weights of users, it starts federated aggregations and generates a new global model. This new model is distributed among clients. This method provides privacy and security over clients’ data. However, In the malicious environment, where malicious clients trained with corrupted data send their weights to a centralized server, global model aggregation degrades because of harmful weights. Also, in a collaboration environment, where clients can send and receive data from other clients, malicious clients can send their corrupted data to benign clients, negatively affecting their local training. In the end, those clients might turn into partially malicious clients. In this work, we improve our previous research, score-based aggregation (SBA), and develop a malicious-client elimination algorithm before aggregation. With this algorithm, global model training can be converged higher scores according to unseen validation data scores even if there are 50% malicious rate among clients.