FedP3: Federated Personalized and Privacy-friendly Network Pruning under
  Model Heterogeneity
Kai Yi Nidham Gazagnadou Peter Richtárik Lingjuan Lyu
The interest in federated learning has surged in recent research due to its
unique ability to train a global model using privacy-secured information held
locally on each client. This paper pays particular attention to the issue of
client-side model heterogeneity, a pervasive challenge in the practical
implementation of FL that escalates its complexity. Assuming a scenario where
each client possesses varied memory storage, processing capabilities and
network bandwidth - a phenomenon referred to as system heterogeneity - there is
a pressing need to customize a unique model for each client. In response to
this, we present an effective and adaptable federated framework FedP3,
representing Federated Personalized and Privacy-friendly network Pruning,
tailored for model heterogeneity scenarios. Our proposed methodology can
incorporate and adapt well-established techniques to its specific instances. We
offer a theoretical interpretation of FedP3 and its locally
differential-private variant, DP-FedP3, and theoretically validate their
efficiencies.