Federated learning (FL) enables collaborative model training without centralizing data. In multi-domain scenarios with non-identically and independently distributed (non-IID) data, prediction performance is often hindered by catastrophic forgetting of specialized local knowledge and negative transfer from conflicting client updates. To address these challenges, we propose a personalized FL framework with dual-branch (pFedDB) structure and a two-phase training protocol. The dual-branch architecture separates the model into a shared branch for cross-client aggregation and a private branch that remains on each local client. The private branch is never overwritten by server updates, which prevents the catastrophic forgetting of domain-specific knowledge. This structure also significantly reduces communication overhead per round as only the shared branch is transmitted. To mitigate negative transfer, our two-phase protocol first establishes a personalized knowledge anchor by training a single-branch expert model on each client’s local data. In the second phase, the locally trained model is cloned to initialize private and shared branches. Only the shared branch is aggregated in federated training. This process enables the shared branch to learn a general representation that complements the established local expertise. This design consistently improves the performance of every client over its single-domain baseline, overcoming the challenge of negative transfer among clients. Experiments on our new Chest-X-Ray-4 suite and three public benchmarks show that the proposed pFedDB method obtains 30% saving in communication overhead per round and competitive or better accuracy performance than recent FL methods.