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.

本研究关注于联邦学习中的客户端模型异质性问题，并提出了适用于模型异质性场景的有效、适应性强的联邦框架 FedP3 及其差分隐私变体 DP-FedP3，并从理论上验证了它们的高效性。