Federated Learning Aggregation: New Robust Algorithms with Guarantees

  title={Federated Learning Aggregation: New Robust Algorithms with Guarantees},
  author={Adnane Mansour and Gaia Carenini and Alexandre Duplessis and David Naccache},
Federated Learning has been recently proposed for distributed model training at the edge. The principle of this approach is to aggregate models learned on distributed clients to obtain a new more general “average” model (FedAvg). The resulting model is then redistributed to clients for further training. To date, the most popular federated learning algorithm uses coordinate-wise averaging of the model parameters for aggregation. In this paper, we carry out a complete general mathematical… 
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The MNIST Database of Handwritten Digit Images for Machine Learning Research [Best of the Web]
  • L. Deng
  • Computer Science
    IEEE Signal Processing Magazine
  • 2012
In this issue, “Best of the Web” presents the modified National Institute of Standards and Technology (MNIST) resources, consisting of a collection of handwritten digit images used extensively in