Federated Meta Learning for Cross-Domain Personalization with Partial Model Initialization

With the rapid deployment of edge devices, there has been growing concern over the privacy preservation of clients’ private data. Federated learning (FL) has emerged as a promising solution that enables edge devices for collaborative model training without compromising data privacy, as the local datasets of clients are not shared directly. However, previous works have shown that the performance of conventional FL approaches significantly deteriorates when the data distributions across clients are heterogeneous or non-IID. Integrating personalization into FL addresses these challenges by adapting the global model to local data distributions, but existing personalized FL (PFL) frameworks often fail to generalize when clients’ datasets have cross-domain features. One approach to mitigate this challenge is partial model initialization, where to update clients’ local model only a subset of model parameters are initialized from the global model while retaining local parameters. However, obtaining effective generalization across distinct data distributions remains a challenge. In such instances, meta-learning is preferred as it provides the adaptation of a global model across distinct data distributions and tasks by employing only a small number of data samples. In this paper, we propose MetaPartialFL, a novel framework that integrates meta-learning with partial model initialization to address cross-domain personalization and heterogeneity in FL. The approach partitions model parameters into shared global and client-specific local components, enabling effective generalization across clients while maintaining adaptability to each client’s datasets. Extensive evaluations on cross-domain benchmark datasets, such as Office-Home, Office-31, PACS, and DomainNet, demonstrate that MetaPartialFL outperforms state-of-the-art methods, making it a robust and efficient solution for cross-domain FL applications.