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| * **Accelerate Byzantine collaborative learning**: Our recent NeurIPS paper proposed algorithms for collaborative machine learning in the presence of Byzantine nodes, which have been proved to be near optimal with respect to optimality at convergence. However, these algorithms require all-to-all communication at every round, which is suboptimal. This research consists of designing a practical solution to Byzantine collaborative learning, based on the idea of a random communication network at each round, with both theoretical guarantees and practical implementation. Contact [[https://people.epfl.ch/sadegh.farhadkhani?lang=en|Sadegh Farhadkhani]] for more information. | * **Accelerate Byzantine collaborative learning**: Our recent NeurIPS paper proposed algorithms for collaborative machine learning in the presence of Byzantine nodes, which have been proved to be near optimal with respect to optimality at convergence. However, these algorithms require all-to-all communication at every round, which is suboptimal. This research consists of designing a practical solution to Byzantine collaborative learning, based on the idea of a random communication network at each round, with both theoretical guarantees and practical implementation. Contact [[https://people.epfl.ch/sadegh.farhadkhani?lang=en|Sadegh Farhadkhani]] for more information. | ||
| - | * **Decentralize Tournesol’s learning algorithms**: The Tournesol platform leverages the contributions of its community of contributors to assign a « should be more recommended » score to YouTube videos rated by the contributors, using a learning algorithm. Currently, the computations are performed on a central server. But as Tournesol’s user base grows, and as more sophisticated learning algorithms are considered for deployment, there is a growing need to decentralize the computations of the learning algorithm. This project aims to build a framework, which will enable Tournesol users to run part of the computations of Tournesol’s scores directly in their browsers. Contact Lê Nguyên Hoang for more information. | + | * **Decentralize Tournesol’s learning algorithms**: The Tournesol platform leverages the contributions of its community of contributors to assign a « should be more recommended » score to YouTube videos rated by the contributors, using a learning algorithm. Currently, the computations are performed on a central server. But as Tournesol’s user base grows, and as more sophisticated learning algorithms are considered for deployment, there is a growing need to decentralize the computations of the learning algorithm. This project aims to build a framework, which will enable Tournesol users to run part of the computations of Tournesol’s scores directly in their browsers. Contact [[https://people.epfl.ch/le.hoang/?lang=en|Lê Nguyên Hoang]] for more information. |
| * **Listening to the silent majority**: Vanilla machine learning from user-generated data inevitably favors those who generated the most amounts of data. But this means that learning algorithms will be optimized for these users, rather than for the silent majority. This research aims to correct for this bias, by trying to infer what data the majority would have likely generated, and by inferring what the models would have learned if the silent majority’s data was included in the training of the models. It involves both designing algorithms, proving correctness and implementing them. This research is motivated by the Tournesol project. Contact Lê Nguyên Hoang for more information. | * **Listening to the silent majority**: Vanilla machine learning from user-generated data inevitably favors those who generated the most amounts of data. But this means that learning algorithms will be optimized for these users, rather than for the silent majority. This research aims to correct for this bias, by trying to infer what data the majority would have likely generated, and by inferring what the models would have learned if the silent majority’s data was included in the training of the models. It involves both designing algorithms, proving correctness and implementing them. This research is motivated by the Tournesol project. Contact Lê Nguyên Hoang for more information. | ||