NoSQL document databases emerged as an alternative to relational databases for managing large volumes of data. NoSQL document databases ensure big data storage and good query performance and are essential when the data scheme does not fit into the scheme of relational databases. They store their data in the form of documents and can handle unstructured, semi-structured, and structured data. This work evaluates the top three open-source NoSQL document databases: Couchbase, CouchDB, and MongoDB with Yahoo! Cloud Serving Benchmark (YCSB), which has become a standard for NoSQL database evaluation. The performance and scale-up of document databases are assessed using YCSB workloads with a different number of records and threads, where the runtime is measured for each database. In the experimental evaluation, we concluded that MongoDB is the database with the best runtime, except for the workload composed by scan operations. In addition, we identified CouchDB as the database with the best scale-up when varying the number of threads.

Federated learning is a new paradigm where multiple data owners, referred to as clients, work together with a global server to train a shared machine learning model without disclosing their personal training data. Despite its many advantages, the system is vulnerable to client compromise by malicious agents attempting to modify the global model. Several defense algorithms against untargeted and targeted poisoning attacks on model updates in federated learning have been proposed and evaluated separately. This paper compares the performances of six state-of-the art defense algorithms—PCA + K-Means, KPCA + K-Means, CONTRA, KRUM, COOMED, and RPCA + PCA + K-Means. We explore a variety of situations not considered in the original papers. These include varying the percentage of Independent and Identically Distributed (IID) data, the number of clients, and the percentage of malicious clients. This comprehensive performance study provides the results that the users can use to select appropriate defense algorithms to employ based on the characteristics of their federated learning systems.