Abstract Federated Learning (FL) typically operates in a closed-set setting where all test classes are known during training, limiting its applicability in real-world scenarios where models must handle emerging unknown classes. This leads to misclassification of unseen categories as known ones. To address this limitation, we introduce Federated Open-Set Recognition (FedOSR), a novel paradigm enabling distributed clients to collaboratively train models that classify known classes while detecting and rejecting unknown ones. However, FedOSR presents unique challenges: the inter-set interference between learning closed-set and open-set knowledge within each client, and the intra-set inconsistency arising from data heterogeneity across clients. These challenges fundamentally complicate the federated aggregation process, as divergent optimization objectives and heterogeneous data distributions lead to parameter misalignment during model aggregation. In this work, we propose FedPD++ , a parameter disentanglement guided framework that systematically addresses both challenges through coordinated client-server mechanisms. On the client side, Local Parameter Disentanglement (LPD) decouples each OSR model into task-specific closed-set and open-set subnetworks to prevent inter-set interference. We introduce a Dynamic Path Integral (DPI) score that robustly identifies task-relevant parameters by leveraging path integral stability, coupled with an Adaptive Soft Masking (ASM) strategy that creates flexible subnetworks with adaptive thresholds rather than rigid binary partitions. On the server side, Global Divide-and-Conquer Aggregation (GDCA) tackles intra-set inconsistency by partitioning each subnetwork into shared and specific components, then aligning corresponding parts across clients using optimal transport to eliminate parameter misalignment. To ensure stable aggregation, we integrate Sequential Batch-Norm Alignment (SBA) that leverages temporal batch normalization statistics from multiple clients. Extensive experiments on open-set classification and segmentation tasks demonstrate that FedPD++ consistently achieves significant performance improvements over state-of-the-art methods. Code is available at: https://github.com/CUHK-AIM-Group/FedPD
Yang et al. (Mon,) studied this question.