Field-Based Coordination for Federated Learning
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Springer Nature Switzerland
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https://link.springer.com/content/pdf/10.1007/978-3-031-62697-5_4
Reference40 articles.
1. Aguzzi, G.: Research directions for aggregate computing with machine learning. In: El-Araby, E., et al. (eds.) IEEE International Conference on Autonomic Computing and Self-organizing Systems, ACSOS 2021, Companion Volume, Washington, DC, USA, 27 September–1 Octoter 2021, pp. 310–312. IEEE (2021). https://doi.org/10.1109/ACSOS-C52956.2021.00078
2. Aguzzi, G., Casadei, R., Pianini, D., Viroli, M.: Dynamic decentralization domains for the internet of things. IEEE Internet Comput. 26(6), 16–23 (2022). https://doi.org/10.1109/MIC.2022.3216753
3. Aguzzi, G., Casadei, R., Viroli, M.: Addressing collective computations efficiency: towards a platform-level reinforcement learning approach. In: Casadei, R., et al. (eds.) IEEE International Conference on Autonomic Computing and Self-Organizing Systems, ACSOS 2022, Virtual, CA, USA, 19–23 September 2022, pp. 11–20. IEEE (2022). https://doi.org/10.1109/ACSOS55765.2022.00019
4. Aguzzi, G., Casadei, R., Viroli, M.: Machine learning for aggregate computing: a research roadmap. In: 42nd IEEE International Conference on Distributed Computing Systems, ICDCS Workshops, Bologna, Italy, 10 July 2022, pp. 119–124. IEEE (2022). https://doi.org/10.1109/ICDCSW56584.2022.00032
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