Abstract: Federated learning (FL) is an innovative privacy-preserving machine learning paradigm that enables clients to train a global model without sharing their local data. However, the coexistence ...

Abstract: Existing works on data trading often overlook the impact of data freshness on its valuation. This paper explores a fresh data market, where a platform offers data with varying freshness ...

Objective: This study aimed to determine optimal sample sizes and the relationships between sample size and dataset-level characteristics over a variety of binary classification algorithms. Methods: A ...