Decoupling Shared and Personalized Knowledge: A Dual-Branch Federated Learning Framework for Multi-Domain with Non-IID Data - Pang, Yiran; Ni, Zhen; Zhong, Xiangnan (Proceedings of the AAAI Conference on Artificial Intelligence, 2026)
Federated learning (FL) enables collaborative model trainingwithout centralizing data. In multi-domain scenarios withnon-identically and independently distributed (non-IID) data,prediction performance is often hindered by catastrophic forgettingof specialized local knowledge and negative transferfrom conflicting client updates. To address these challenges,we propose a personalized FL framework with dual-branch(pFedDB) structure and a two-phase training protocol. Thedual-branch architecture separates the model into a sharedbranch for cross-client aggregation and a private branch thatremains on each local client. The private branch is never overwrittenby server updates, which prevents the catastrophicforgetting of domain-specific knowledge. This structure alsosignificantly reduces communication overhead per round asonly the shared branch is transmitted. To mitigate negativetransfer, our two-phase protocol first establishes a personalizedknowledge anchor by training a single-branch expertmodel on each client’s local data. In the second phase, the locallytrained model is cloned to initialize private and sharedbranches. Only the shared branch is aggregated in federatedtraining. This process enables the shared branch to learn ageneral representation that complements the established localexpertise. This design consistently improves the performanceof every client over its single-domain baseline, overcomingthe challenge of negative transfer among clients. Experimentson our new Chest-X-Ray-4 suite and three public benchmarksshow that the proposed pFedDB method obtains 30% savingin communication overhead per round and competitive or betteraccuracy performance than recent FL methods.