In recent years, Federated Domain Generalization (FedDG) has succeeded in generalizing to unknown clients (domains). However, current methods only utilize training data, and when there is a significant difference between the unknown client and source client domains (domain shift), these methods cannot ensure model performance. This limitation appears to have caused research in FedDG to reach a bottleneck. On the other hand, test data is a resource that can help models adapt while previous FedDG approaches have not taken this into account. In this paper, we introduce a new framework TTA-FedDG to address the FedDG problem, which leverages test-time adaptation (TTA) to adapt across different domains, thereby enhancing the generalization of the model. We propose the method Federated domain generalization based on select Strong Pseudo Label (FedSPL), which combines fast feature matching and knowledge distillation. Our method consists of two parts. Firstly, we use fast feature reordering for feature mixing during local updates on the client side, improving the robustness of the global model and enhancing its generalization ability to mitigate domain shift. Secondly, we employ a teacher-student model with contrastive learning and label selection during the testing phase, enabling the global model to better adapt to the distribution of the target client,thereby alleviating domain shift. Extensive experiments havedemonstrated the effectiveness of FedSPL in handling domain shift, outperforming existing FedDG methods across multiple datasets and model architectures.

Black-box Domain Adaptation (BDA) utilizes a black-box predictor of the source domain to label target domain data, addressing privacy concerns in Unsupervised Domain Adaptation (UDA). However, BDA assumes identical label sets across domains, which is unrealistic. To overcome this limitation, we propose a study on BDA with unknown classes in the target domain. It uses a black-box predictor to label target data and identify "unknown" categories, without requiring access to source domain data or predictor parameters, thus addressing both data privacy and category shift issues in traditional UDA. Existing methods face two main challenges: (i) Noisy pseudo-labels in knowledge distillation (KD) accumulate prediction errors, and (ii) relying on a preset threshold fails to adapt to varying category shifts. To address these, we propose ADU, a framework that allows the target domain to autonomously learn pseudo-labels guided by quality and use an adaptive threshold to identify "unknown" categories. Specifically, ADU consists of Selective Amplification Knowledge Distillation (SAKD) and Entropy-Driven Label Differentiation (EDLD). SAKD improves KD by focusing on high-quality pseudo-labels, mitigating the impact of noisy labels. EDLD categorizes pseudo-labels by quality and applies tailored training strategies to distinguish "unknown" categories, improving detection accuracy and adaptability. Extensive experiments show that ADU achieves state-of-the-art results, outperforming the best existing method by 3.1% on VisDA in the OPBDA scenario.

Grounded Multimodal Named Entity Recognition (GMNER) is a new information extraction task.It requires models to extract named entities and ground them to real-world visual objects.Previous methods, relying on domainspecific fine-tuning, struggle with unseen multimodal entities due to limited knowledge and generalization.Recently, multimodal large language models (MLLMs) have demonstrated strong open-set abilities.However, their performance is hindered by the lack of in-domain knowledge due to costly training for GMNER datasets.To address these limitations, we propose UnCo, a two-stage Uncertainty-driven Collaborative framework that leverages the complementary strengths of small fine-tuned models and MLLMs.Specifically, in stage one, we equip the small model with a unified uncertainty estimation (UE) for multimodal entities.This enables the small model to express "I do not know" when recognizing unseen entities beyond its capabilities.Predictions with high uncertainty are then filtered and delegated to the MLLM.In stage two, an Uncertainty-aware Hierarchical Correction mechanism guides the MLLM to refine uncertain predictions using its open-domain knowledge.Ultimately, UnCo effectively retains the in-domain knowledge of small models while utilizing the capabilities of MLLMs to handle unseen samples.Extensive experiments demonstrate UnCo's effectiveness on two GMNER benchmarks.

Concerns about privacy and the centralized collection of sensitive data have led to the development of Federated Learning, a paradigm enabling collaborative model training without the need to aggregate raw data centrally. However, variations in data distributions between source and target clients, a phenomenon known as domain shift, often lead to degraded model performance. While recent advancements in Federated Domain Generalization address this challenge, they typically operate under a closed-set assumption, disregarding scenarios where target domains introduce entirely new classes, referred to as category shift. This oversight can result in critical misclassifications in real-world applications. To overcome these limitations, we explore Federated Open-Set Domain Generalization (FedOSDG) setting for the first time, which not only preserves data privacy but also identifies new, unseen classes in the unseen target domains. Specifically, we propose the Adaptive Adjustment Boundary and Weights (AABAW) framework, comprising Stronger Classification Boundary (SCB) and Adaptive Adjustment of Weights (AAW). The SCB module reinforces the decision boundaries of the binary classifiers to handle category shift while the AAW module leverages local model diversity to increase the variance of global model, thereby enhancing the model’s generalization under domain shifts. Experimental results show that our proposed AABAW achieves state-of-the-art performance in recognizing unknown classes and H-scores in the FedOSDG task with considerable gains and maintains competitive performance in all classes.

In response to the growing demands of real-world applications, models must be capable of learning continuously under inconsistent data distribution. However, existing Class-Incremental (CI) methods fail to alleviate domain shifts, while traditional Unsupervised Domain Adaptation (UDA) techniques suffer from catastrophic forgetting and privacy concerns. To address these limitations, we explore Source-Free Class Incremental Domain Adaptation (SFCIDA) and propose a novel approach, Quantifying Samples with Invariance (QSI), for this scenario. Our proposed method involves two main strategies: (1) Semantic Restructuring. We identify confusing source category pairs and restructure images to create a negative dataset that is semantically similar to the source features, refining accurate decision boundary among source categories. (2) Invariance Quantification. The sample's confidence is then quantified by its spatial location under the special data distribution, reflecting the trade-off between invariant features and domain shifts. Guided by such strategy, samples' confidence is accumulated for the target model to prioritize reliable categories, not only mitigating the poor performance of experience replay in unsupervised scenarios, but alleviating distribution discrepancies simultaneously. Experiments demonstrate that our approach outperforms previous methods, establishing new state-of-the-art performance on the Office-31, Office-Home and DomainNet-126 datasets, with average accuracy improvements of over 7.3%, 4.9% and 10.2% respectively.

Federated learning (FL) has recently emerged as the primary approach to overcoming data silos, enabling collaborative model training without sharing sensitive or proprietary data. Parallel federated learning (PFL) aggregates models trained independently on each client’s local data, which can lead to suboptimal convergence due to limited data exposure. In contrast, Sequential Federated Learning (SFL) allows models to traverse client datasets sequentially, enhancing data utilization. However, SFL effectiveness is limited in real-world non-IID scenarios characterized by category shift (inconsistent class distributions) and domain shift (distribution discrepancies). These shifts cause two critical issues: update order sensitivity, where model performance varies significantly with the sequence of client updates, and catastrophic forgetting, where the model forgets previously learned features when trained on new client data. We propose SPFL, a novel updating method that can be integrated into existing FL methods, integrating sequential updates with parallel aggregation to enhance data utilization and ease update order sensitivity. At the same time, we give the convergence analysis of SPFL under strong convex, general convex, and non-convex conditions, proving that this update scheme is significantly better than PFL and SFL. Additionally, we introduce the Global-Local Alignment Module to mitigate catastrophic forgetting by aligning the predictions of the global model with those of the local and previous models during training. Our extensive experiments demonstrate that integrating SPFL into existing PFL methods significantly improves performance under category and domain shifts.