Corpus ID: 226307110

Heterogeneous Data-Aware Federated Learning

@article{Yang2020HeterogeneousDF,
  title={Heterogeneous Data-Aware Federated Learning},
  author={Lixuan Yang and Cedric Beliard and Dario Rossi},
  journal={ArXiv},
  year={2020},
  volume={abs/2011.06393}
}
Federated learning (FL) is an appealing concept to perform distributed training of Neural Networks (NN) while keeping data private. With the industrialization of the FL framework, we identify several problems hampering its successful deployment, such as presence of non i.i.d data, disjoint classes, signal multi-modality across datasets. In this work, we address these problems by proposing a novel method that not only (1) aggregates generic model parameters (e.g. a common set of task generic NN… Expand
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SHOWING 1-10 OF 18 REFERENCES
Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints
  • 49
  • Highly Influential
  • PDF
Federated Learning with Non-IID Data
  • 354
  • PDF
LEAF: A Benchmark for Federated Settings
  • 186
  • Highly Influential
  • PDF
Federated Learning of Deep Networks using Model Averaging
  • 285
  • Highly Influential
  • PDF
Think Locally, Act Globally: Federated Learning with Local and Global Representations
  • 36
  • Highly Influential
  • PDF
Fair Resource Allocation in Federated Learning
  • 112
  • PDF
Large scale distributed neural network training through online distillation
  • 174
  • PDF
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