Federated recommender system is emerging as a new paradigm for providing personalized services while preserving user data privacy. Most existing personalized federated recommender systems predict the user's next item by discretely training user and item embeddings. However, this training approach overlooks the user's behavioral patterns, suffers from low interpretability, and requires a substantial amount of data and meticulous fine-tuning to achieve stable and accurate embeddings. To address these limitations, we propose Federated Context-Aware Personalized Recommendation (FedCAR), a novel framework that leverages users’ recent interactions as behavioral context to guide prediction. Instead of static user embeddings, FedCAR dynamically constructs context representations by aggregating and weighting recently interacted item embeddings. Additionally, we incorporate a contrastive learning strategy that enables the model to capture shared behavioral structures across clients while maintaining personalized preferences, enhancing both generalization and robustness in heterogeneous settings. Experiments on 5 benchmark datasets show that FedCAR consistently outperforms state-of-the-art methods and provides interpretable recommendations by explicitly modeling context dependencies.

Existing weakly supervised temporal action localization (WTAL) methods typically follow a decoupled classification-localization pipeline: segment-level classifiers are trained first, and their predictions are then aggregated to score proposals at inference. Under this training-inference discrepancy, proposal scoring at inference relies on an additional aggregation step, which can accumulate errors from noisy segment responses and thus undermine score reliability. Moreover, proposal scores are often directly used as confidence without explicit score-quality modeling or quality-aware evaluation, further contributing to pronounced score-quality misalignment and thus widening the classification-localization gap. To address proposal score-quality misalignment, we propose ACL-Net, a framework for proposal score calibration. At its core is a dual-axis Proposal-level Action Consistency Learning (PACL) paradigm, implemented through two complementary modules: (i) a Semantic Consistency Module (SCM) that refines proposal representations by maintaining fused class centers to enforce compact and robust same-class features; within SCM, a cross-modal consistency-driven Classification Enhancement Module (CEM) denoises the fused class centers to mitigate error accumulation under weak supervision; and (ii) a Process Consistency Module (PCM) that derives geometry-aware reference scores from relative temporal relations among overlapping proposals, guiding the model to assess proposal quality in terms of relative process completeness and improve score-quality alignment. By jointly modeling semantic and process consistency to calibrate proposal scores, ACL-Net markedly improves localization accuracy. On THUMOS14 and ActivityNet1.3, it achieves state-of-the-art performance with uniform and substantial gains across multiple established baselines, while markedly lowering the expected calibration error (ECE).

Sequential recommendation aims to derive insights from user interaction records and make predictions based on relationships between users and items. However, most existing approaches do not effectively integrate user intents and preferences, which limits their capability to capture user behavior patterns. Additionally, these methods often struggle with poor performance in sparse data scenarios. To address these challenges, we proposeEL4SR, a sequential recommendation approach by integrating intent understanding and preference learning.EL4SRsimultaneously learns user intents and preferences through a dual-channel recommendation module, modeling both item and popularity sequences to enable mutual learning that captures the combined effects of intent and preference. Moreover, we enhance intent learning through contrastive learning, improving adaptability in sparse data contexts. We design several augmentation operators to improve the performance and robustness ofEL4SR. Extensive experiments on MovieLens and Amazon demonstrate the performance of our proposed method across various scenarios.

Microservice-based systems often suffer from reliability issues due to their intricate interactions and expanding scale. With the rapid growth of observability techniques, various methods have been proposed to achieve failure diagnosis, including root cause localization and failure type identification, by leveraging diverse monitoring data such as logs, metrics, or traces. However, traditional failure diagnosis methods that use single-modal data can hardly cover all failure scenarios due to the restricted information. Several failure diagnosis methods have been recently proposed to integrate multimodal data based on deep learning. These methods, however, tend to combine modalities indiscriminately and treat them equally in failure diagnosis, ignoring the relationship between specific modalities and different diagnostic tasks. This oversight hinders the effective utilization of the unique advantages offered by each modality. To address the limitation, we propose TVDiag , a multimodal failure diagnosis framework for locating culprit microservice instances and identifying their failure types (e.g., Net-packets Corruption) in microservice-based systems. TVDiag employs task-oriented learning to enhance the potential advantages of each modality and establishes cross-modal associations based on contrastive learning to extract view-invariant failure information. Furthermore, we develop a graph-level data augmentation strategy that randomly inactivates the observability of some normal microservice instances to mitigate the shortage of training data. Experimental results on four datasets show that TVDiag outperforms the state-of-the-art methods in multimodal failure diagnosis by at least 20.16% and 3.08% in terms of \(HR@1\) and F1-score, respectively.

With the development of federated learning techniques and the increased need for user privacy protection, the federated recommendation has become a new recommendation paradigm. However, most existing works focus on user-level federated recommendation, leaving platform-level federated recommendation largely unexplored. A significant challenge in platform-level federated recommendation scenarios is severe label skew. Users behave in various ways on different platforms, bringing up the rating and item bias problem. In this work, we propose FREIB (Federated Recommendation with Explicitly Encoding Item Bias). The core idea is explicitly encoding item bias during federated learning, addressing the problem of fuzzy item bias, and achieving consistent representation in label skew scenarios. We achieve this by utilizing global knowledge guidance to model common rating patterns and by aligning feature prototypes to enhance item encoding at the same rating level. Extensive experiments conducted on three public datasets demonstrate the superiority of our method over several state-of-the-art approaches.

Accurate vessel segmentation is essential for diagnosing and managing vascular and ophthalmic diseases. Traditional learning-based vessel segmentation methods heavily rely on high-quality, pixel-level annotated datasets. However, segmentation performance suffers significantly when applied in federated learning settings due to vessel morphology inconsistency and vessel-background imbalance. The former limits the ability of models to capture fine-grained vessels, while the latter overemphasizes background pixels and biases the model towards them. To address these challenges, we propose a novel method named Federated Vessel-Aware Calibration (FVAC), which leverages global uncertainty to provide differentiated guidance for clients, focusing on pixels of various morphologies that are difficult to distinguish. Furthermore, we introduce a foreground-background decoupling alignment strategy that utilizes more stable and balanced global features to mitigate semantic drift caused by vessel-background imbalance in local clients. Comprehensive experiments confirm the effectiveness of our method

The Vision Language Model (VLM) excels in aligning vision and language representations, and prompt learning has emerged as a key technique for adapting such models to downstream tasks. However, the application of prompt learning with VLM in federated learning (FL) scenarios remains underexplored. This paper systematically investigates the behavioral differences between language prompt learning (LPT) and vision prompt learning (VPT) under data heterogeneity challenges, including label skew and domain shift. We conduct extensive experiments to evaluate the impact of various FL and prompt configurations, such as client scale, aggregation strategies, and prompt length, to assess the robustness of Federated Prompt Learning (FPL). Furthermore, we explore strategies for enhancing prompt learning in complex scenarios where label skew and domain shift coexist, including leveraging both prompt types when computational resources allow. Our findings offer practical insights into optimizing prompt learning in federated settings, contributing to the broader deployment of VLMs in privacy-preserving environments.