Traditional page recommendation models are endangered by stricter privacy regulations, such as the General Data Protection Regulation (GDPR). The performance of these models suffer when only a part of the users share their personal data, such as cookies, with web servers, while the rest of the users choose to opt-out from sharing these data. Furthermore, these models are not designed to provide recommendations for users who do not share their data. This paper addresses the question of how to provide good page recommendations to all users, independent of their privacy attitudes. We propose Fed4Rec, a privacy-preserving framework for page recommendation based on federated learning (FL) and model-agnostic meta-learning (MAML), which allows machine learning models to train on data collected from both public users, who share data with the server, and private users, who do not share data with the server. Fed4Rec enables recommendations for both public users, computed at the server, and private users, computed at their local devices. Private users' data are stored only on user devices and never shared with the server. FL is used to train on local data, and Fed4Rec shares with the server only partial model parameters, computed on local devices. MAML is used to jointly train on the public data and the model parameters from the private users. We compare Fed4Rec against several baseline frameworks, using a publicly available dataset from a large news portal. The results show that Fed4Rec outperforms the baselines in terms of recommendation accuracy. We also conduct one ablation study to examine the impact of varying the ratio between the number of public and private users. Fed4Rec performs better than the baselines for all ratios, but it is especially beneficial w hen t he p ercentage of public users is low.