| 1. |
- Ilić, Mihailo, et al.
(författare)
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Towards optimal learning : Investigating the impact of different model updating strategies in federated learning
- 2024
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Ingår i: Expert systems with applications. - : Elsevier. - 0957-4174 .- 1873-6793. ; 249:Part A
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Tidskriftsartikel (refereegranskat)abstract
- With rising data security concerns, privacy preserving machine learning (ML) methods have become a key research topic. Federated learning (FL) is one such approach which has gained a lot of attention recently as it offers greater data security in ML tasks. Substantial research has already been done on different aggregation methods, personalized FL algorithms etc. However, insufficient work has been done to identify the effects different model update strategies (concurrent FL, incremental FL, etc.) have on federated model performance. This paper presents results of extensive FL simulations run on multiple datasets with different conditions in order to determine the efficiency of 4 different FL model update strategies: concurrent, semi -concurrent, incremental, and cyclic -incremental. We have found that incremental updating methods offer more reliable FL models in cases where data is distributed both evenly and unevenly between edge nodes, especially when the number of data samples across all edge nodes is small.
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| 2. |
- Savic, Milos, et al.
(författare)
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The Application of Machine Learning Techniques in Prediction of Quality of Life Features for Cancer Patients
- 2023
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Ingår i: Computer Science and Information Systems. - : ComSIS Consortium. - 1820-0214. ; 20:1, s. 381-404
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Tidskriftsartikel (refereegranskat)abstract
- Quality of life (QoL) is one of the major issues for cancer patients. With the advent of medical databases containing large amounts of relevant QoL infor-mation it becomes possible to train predictive QoL models by machine learning (ML) techniques. However, the training of predictive QoL models poses several challenges mostly due to data privacy concerns and missing values in patient data. In this paper, we analyze several classification and regression ML models predicting QoL indicators for breast and prostate cancer patients. Three different approaches are employed for imputing missing values, and several settings for data privacy pre-serving are tested. The examined ML models are trained on datasets formed from two databases containing a large number of anonymized medical records of can-cer patients from Sweden. Two learning scenarios are considered: centralized and federated learning. In the centralized learning scenario all patient data coming from different data sources is collected at a central location prior to model training. On the other hand, federated learning enables collective training of machine learning models without data sharing. The results of our experimental evaluation show that the predictive power of federated models is comparable to that of centrally trained models for short-term QoL predictions, whereas for long-term periods centralized models provide more accurate QoL predictions. Furthermore, we provide insights into the quality of data preprocessing tasks (missing value imputation and differen-tial privacy).
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