Decentralised Federated Learning in a Dynamic
il y a 3 semaines
**Domaine**:
Mathématiques, information scientifique, logiciel
**Contrat**:
Stage
**Intitulé de l'offre**:
Decentralised federated learning in a dynamic environment H/F
**Sujet de stage**:
Federated learning was introduced in 2016 by Google [1] as a new machine learning paradigm where multiple entities (clients) collaborate in solving a machine learning problem under the coordination of a central server. Each client trains a local model using its private data, and only model parameters are exchanged between the clients and the server, without exposing clients' private data [2]. However, the original architecture of federated learning (centralised federated learning) is highly dependent on a central server for orchestrating the training process. The objective of this internship is to investigate the solutions for enabling decentralised learning without the central server, taking into account the challenges including statistical heterogeneity and dynamics of the learning environment.
**Durée du contrat (en mois)**:
6
**Description de l'offre**:
The internship will proceed as follows:
- Conduct a literature review on DFL to identify the fundamental considerations for DFL (e.g., architectures, network topologies, optimisation and aggregation algorithms, communication mechanisms);
- Identify the impacts of statistical heterogeneity and system dynamics on DFL;
- Study the state-of-the-art solutions to mitigate the identified impacts;
- Conduct an empirical evaluation of the solutions.
[1] McMahan, B., Moore, E., Ramage, D., Hampson, S. and y Arcas, B.A., 2017, April. Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics (pp. 1273-1282). PMLR.
[2] Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R. and D’Oliveira, R.G., 2021. Advances and open problems in federated learning. Foundations and Trends® in Machine Learning, 14(1-2), pp.1-210.
[3] Gabrielli, E., Pica, G. and Tolomei, G., 2023. A survey on decentralized federated learning. arXiv preprint arXiv:2308.04604.
[4] Yuan, L., Sun, L., Yu, P.S. and Wang, Z., 2023. Decentralized Federated Learning: A Survey and Perspective. arXiv preprint arXiv:2306.01603.
[5] Beltrán, E.T.M., Pérez, M.Q., Sánchez, P.M.S., Bernal, S.L., Bovet, G., Pérez, M.G., Pérez, G.M. and Celdrán, A.H., 2023. Decentralized federated learning: Fundamentals, state of the art, frameworks, trends, and challenges. IEEE Communications Surveys & Tutorials.
**Localisation du poste**:
**Site**:
Saclay
**Localisation du poste**:
France, Ile-de-France, Essonne (91)
**Ville**:
Gif-sur-Yvette
**Demandeur**:
**Disponibilité du poste**:
01/03/2024
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