Jenny Hamer
I work in fundamental machine learning research areas and apply machine learning to problems in climate and weather. My research interests lie predominantly within machine learning and optimization, and include federated learning and optimization under heterogeneity; the intersection and trade-offs of fairness, robustness, and other performance metrics; unsupervised and embedding learning; and applying ML to address the climate crisis.
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Communication cost is often a bottleneck in federated learning and other client-based distributed learning scenarios. To overcome this, several gradient compression and model compression algorithms have been proposed. In this work, we propose an alternative approach whereby an ensemble of pre-trained base predictors is trained via federated learning. This method allows for training a model which may otherwise surpass the communication bandwidth and storage capacity of the clients to be learned with on-device data through federated learning. Motivated by language modeling, we prove the optimality of ensemble methods for density estimation for standard empirical risk minimization and agnostic risk minimization. We provide communication-efficient ensemble algorithms for federated learning, where per-round communication cost is independent of the size of the ensemble. Furthermore, unlike works on gradient compression, our proposed approach reduces the communication cost of both server-to-client and client-to-server communication.
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