Counterfactual Explanations and Federated Learning for Enhanced Data Analytics Optimisation
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Springer Nature Singapore
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https://link.springer.com/content/pdf/10.1007/978-981-97-0353-1_2
Reference28 articles.
1. Marshoodulla SZ, Saha G (2023) An approach towards removal of data heterogeneity in SDN-based IoT framework. 22:100763. https://doi.org/10.1016/j.iot.2023.100763
2. Chen A, Fu Y, Wang L, Duan G (2022) DWFed: a statistical-heterogeneity-based dynamic weighted model aggregation algorithm for federated learning 16:1041553,a. https://doi.org/10.3389/fnbot.2022.1041553
3. Han J, Khan AF, Zawad S, Anwar A, Angel NB, Zhou Y, Yan F, Butt AR (2022) Heterogeneity-aware adaptive federated learning scheduling. In: 2022 IEEE international conference on big data (big data), pp 911–920. IEEE. ISBN 978-1-66548-045-1. https://doi.org/10.1109/BigData55660.2022.10020721
4. Ilić M, Ivanović M Federated learning—opportunities and application challenges. In: Nguyen NT, Botzheim J, Gulyás L, Nunez M, Treur J, Vossen G, Kozierkiewicz A (eds) Advances in computational collective intelligence. Series Title: Communications in computer and information science, vol 1864, pp 481–492. Springer Nature Switzerland. ISBN 978-3-031-41773-3 978-3-031-41774-0. https://doi.org/10.1007/978-3-031-41774-0_38
5. Chen M, Jiang M, Dou Q, Wang Z, Li X (2023) FedSoup: improving generalization and personalization in federated learning via selective model interpolation. In: Greenspan H, Madabhushi A, Mousavi P, Salcudean S, Duncan J, Syeda-Mahmood T, Taylor R (eds) Medical image computing and computer assisted intervention—MICCAI 2023. Lecture notes in computer science, vol 14221, pp 318–328. Springer Nature Switzerland. ISBN 978-3-031-43894-3 978-3-031-43895-0. https://doi.org/10.1007/978-3-031-43895-0_30
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