Title: Client-agnostic Learning and Zero-shot Adaptation for Federated Domain Generalization. Abstract: Federated domain generalization (federated DG) aims to learn a client-agnostic global model from various distributed source domains and generalize the model to new clients in completely unseen domains. The main challenges of federated DG are the difficulty of building the global model with local client models from different domains while keeping data private and low generalizability to test clients, where data distribution deviates from those of training clients. To solve these challenges, we present two strategies: (1) client-agnostic learning with mixed instance-global statistics and (2) zero-shot adaptation with estimated statistics. In client-agnostic learning, we first augment local features by using data distribution of other clients via global statistics in the global model's batch normalization layers. This approach allows the generation of diverse domains by mixing local and global feature statistics while keeping data private. Local models then learn client-invariant representations by applying our client-agnostic objectives with the augmented data. Next, we propose a zero-shot adapter to help the learned global model to directly bridge a large domain gap between seen and unseen clients. At inference time, the adapter mixes instance statistics of a test input with global statistics that are vulnerable to distribution shift. With the aid of the adapter, the global model improves generalizability further by reflecting test distribution. We comprehensively evaluate our methods on several benchmarks in federated DG.