  Federated learning (FL) is recently surging as a promising decentralized deep
learning (DL) framework that enables DL-based approaches trained
collaboratively across clients without sharing private data. However, in the
context of the central party being active and dishonest, the data of individual
clients might be perfectly reconstructed, leading to the high possibility of
sensitive information being leaked. Moreover, FL also suffers from the
nonindependent and identically distributed (non-IID) data among clients,
resulting in the degradation in the inference performance on local clients'
data. In this paper, we propose a novel framework, namely Personalized
Privacy-Preserving Federated Learning (PPPFL), with a concentration on
cross-silo FL to overcome these challenges. Specifically, we introduce a
stabilized variant of the Model-Agnostic Meta-Learning (MAML) algorithm to
collaboratively train a global initialization from clients' synthetic data
generated by Differential Private Generative Adversarial Networks (DP-GANs).
After reaching convergence, the global initialization will be locally adapted
by the clients to their private data. Through extensive experiments, we
empirically show that our proposed framework outperforms multiple FL baselines
on different datasets, including MNIST, Fashion-MNIST, CIFAR-10, and CIFAR-100.
