Background: Transferring data across nodes could raise concerns about data security and privacy. Federated learning is a tech- nological remedy for these problems. However, in a real federated scenario, there are two main issues: 1) it is difficult to collect locally a large and representative dataset, and 2) the data are non-stationary, where non-stationary means that the data increase in size over time, which may generate catastrophic forgetting events affecting the learning process. Objective: The aim of this paper is to investigate and assess the performance and behavior of a federated model during the occurrence of catastrophic forgetting events within the context of a non-stationary data scenario. Methods: To achieve this objective, the performance of a proposed federated learning approach has been evaluated in terms of the continuous flow of data in order to train a time series-based model. Results and Conclusion: The results demonstrate the goodness of the solution in terms of performance, with improvements ranging from 2% to 28%. This indicates the benefits that the proposed approach brings to a node in terms of recovering from a catastrophic forgetting event, also taking into account the fact that the probability of catastrophic forgetting is higher at the beginning of the learning process.
A federated learning-based approach to recognize subjects at a high risk of hypertension in a non-stationary scenario
Paragliola;Giovanni
2023
Abstract
Background: Transferring data across nodes could raise concerns about data security and privacy. Federated learning is a tech- nological remedy for these problems. However, in a real federated scenario, there are two main issues: 1) it is difficult to collect locally a large and representative dataset, and 2) the data are non-stationary, where non-stationary means that the data increase in size over time, which may generate catastrophic forgetting events affecting the learning process. Objective: The aim of this paper is to investigate and assess the performance and behavior of a federated model during the occurrence of catastrophic forgetting events within the context of a non-stationary data scenario. Methods: To achieve this objective, the performance of a proposed federated learning approach has been evaluated in terms of the continuous flow of data in order to train a time series-based model. Results and Conclusion: The results demonstrate the goodness of the solution in terms of performance, with improvements ranging from 2% to 28%. This indicates the benefits that the proposed approach brings to a node in terms of recovering from a catastrophic forgetting event, also taking into account the fact that the probability of catastrophic forgetting is higher at the beginning of the learning process.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.