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Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions
Hossein Zakerinia, Jonathan Scott, Christoph H. Lampert
Personalized federated learning has emerged as a popular approach to training on devices holding statistically heterogeneous data, known as clients. However, most existing approach…
Improved Modelling of Federated Datasets using Mixtures-of-Dirichlet-Multinomials
Jonathan Scott, Áine Cahill
In practice, training using federated learning can be orders of magnitude slower than standard centralized training. This severely limits the amount of experimentation and tuning t…
PeFLL: Personalized Federated Learning by Learning to Learn
Jonathan Scott, Hossein Zakerinia, Christoph H. Lampert
We present PeFLL, a new personalized federated learning algorithm that improves over the state-of-the-art in three aspects: 1) it produces more accurate models, especially in the l…
Cross-client Label Propagation for Transductive and Semi-Supervised Federated Learning
Jonathan Scott, Michelle Yeo, Christoph H. Lampert
We present Cross-Client Label Propagation(XCLP), a new method for transductive federated learning. XCLP estimates a data graph jointly from the data of multiple clients and compute…