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Found 4 papers, 1 papers with code
On the Stability of Iterative Retraining of Generative Models on their own Data
In this paper, we develop a framework to rigorously study the impact of training generative models on mixed datasets -- from classical training on real data to self-consuming generative models trained on purely synthetic data.
Tackling Computational Heterogeneity in FL: A Few Theoretical Insights
In this work, we introduce and analyse a novel aggregation framework that allows for formalizing and tackling computational heterogeneity in federated optimization, in terms of both heterogeneous data and local updates.
FedControl: When Control Theory Meets Federated Learning
To date, the most popular federated learning algorithms use coordinate-wise averaging of the model parameters.
Federated Learning Aggregation: New Robust Algorithms with Guarantees
The resulting model is then redistributed to clients for further training.
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