FedP3: Federated Personalized and Privacy-friendly Network Pruning under Model Heterogeneity
<|reference_start|>FedP3: Federated Personalized and Privacy-friendly Network Pruning under Model Heterogeneity: 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.<|reference_end|>
@article{yi2024fedp3:,
    title={FedP3: Federated Personalized and Privacy-friendly Network Pruning under
  Model Heterogeneity},
    author={Kai Yi, Nidham Gazagnadou, Peter Richt'arik, Lingjuan Lyu},
    journal={arXiv preprint arXiv:2404.09816},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.09816},
    primaryClass={cs.LG cs.CR}
}