Different from existing vertical federated learning (VFL), zero-shot VFL (ZVFL) is an under-explored scenario where test classes are absent from partial parties' training sets. In extreme cases, some test classes even have no training samples for all parties. Traditionally, existing zero-shot methods require abundant seen-class samples for effective knowledge transfer to recognize unseen classes. However, both the limited aligned samples and different seen classes pose several unique challenges to ZVFL. The primary challenge lies in the seen-to-unseen transfer insufficiency, as the scarcity of aligned samples and diverse seen-class distributions across parties severely limits the model's capability to learn discriminative features that can generalize to unseen classes. Moreover, the multi-party bias inconsistency arises as different parties tend to be biased towards their own seen classes during prediction, leading to skewed classification results at the active party. To address these challenges, we propose FEZE, an alignment-flexible zero-shot vertical federated learning framework. Specifically, we introduce a relation learning network to capture class-feature relationships between class labels and feature representations across heterogeneous feature spaces, enabling unseen class recognition through relationship inference. Additionally, we design a meta-relation learning mechanism that leverages diverse class-feature patterns to tackle the insufficient feature generalization from limited seen-class samples. Finally, we propose an alignment-flexible adaptive aggregation strategy that achieves adaptively aggregation based on inconsistent prediction spaces with arbitrary number of aligned samples. Theoretical analysis proves that FEZE can achieve a convergence rate of O(1/T). In the most challenging zero-shot scenario without aligned samples, FEZE surpasses state-of-the-art baselines by an average of 7.47% across three datasets.

Federated Learning (FL) has been widely adopted as a distributed machine learning paradigm aiming to derive a global model without transferring local data to the server. In the context of heterogeneous environments typical of many FL deployments, our research has identified the performance oscillation problem in existing FL methods, resulting in slow convergence and severe performance drop. In this article, we first investigate the global optimizing objective in FL and demonstrate that, due to data heterogeneity and partial client participation, the global updates in a single training epoch may diverge from the intended objectives of conventional FL methods. To address this problem, we introduce a triple-objective decomposition mechanism to decompose the overarching global objective into three distinct local objectives aimed at aligning client gradients. Subsequently, we propose a gradient trajectory smoothing technique known as FedGTS, which refines local updates by estimating a pseudo-gradient leveraging historical global update trajectories. This approach is designed to mitigate performance oscillations and enhance the stability of the learning process. We theoretically demonstrate that our approach reduces variance of local updates and achieves a guaranteed convergence rate. We experimentally show that the proposed method outperforms the baselines with faster convergence and higher accuracy. Extensive experiments validate the effectiveness of the proposed approach across various heterogeneity settings. Our codes are publicly available at GitHub ( https://github.com/ZongHR/FedGTS ).

Next point-of-interest (POI) recommendation has been a hot research topic, which enables new paradigms for kinds of location-based services in real-world scenarios. Due to the privacy concerns and rigorous data regulations, federated learning provides a distributed learning framework to collaboratively train the recommendation model without sharing the highly sensitive POI data with others. However, there exist two main challenges, namelylocation noise, andbalance between personalization and knowledge sharing, seriously restrict the development of the federated next POI recommendation. To this end, in this work, we propose an adaptive clustering based personalized federated learning framework for next POI recommendation with location noise, namedCPF-POI, to address the above challenges. In detail, within the local client, a location recovery module can efficiently remove noises under the given assumption from the noisy POI data in which the recovery error bound can be theoretically proved. Then, within the parameter server, an adaptive clustering scheme is proposed to capture the internal relatedness among all clients to augment positive knowledge sharing. In order to make a balance between personalization and knowledge sharing under personalized federated learning framework, we design an alternative optimization process between clustering similar clients and minimizing local personalized loss functions. Finally, extensive experiments are conducted on two diverse real-world datasets to show the advantages ofCPF-POIover state-of-the-art methods. improvement across all metrics on average.

The robust multivariate time series anomaly detection can facilitate intelligent decisions and timely maintenance in various kinds of monitor systems. However, the robustness is highly restricted by the stochasticity in multivariate time series, which is summarized astemporal stochasticityandspatial stochasticityspecifically. In this paper, we explicitly model the temporal stochasticity variables and the latent graph relationship variables into a unified graphical framework, which can achieve better robustness to dynamicity from both the spatial and temporal perspective. First, within the spatial encoder, every connection exists or not is modeled as a binary stochastic variable, and the graph structure can be learnt automatically. Then, the temporal encoder would embed the highly structured time series into latent stochastic variables to capture both complex temporal dependencies and neighbors information. Moreover, we design a history-future combined anomaly score mechanism with both reconstruction decoder and forecasting decoder to improve the anomaly detection performance. By weighting the historical anomaly factor, the future anomaly factor, and the prediction error of current timestamp, the anomaly detection at current timestamp could be more sensitive to anomaly detection. Finally, extensive experiments on three publicly available anomaly detection datasets demonstrate our proposed method can achieve the best performance in terms of recall and F1 compared with state-of-the-arts baselines.

Due to the privacy concerns and rigorous data regulations, dynamic treatment regimes across hospitals have become increasingly difficult. Fortunately, federated learning provides a distributed learning framework to collaboratively train the model without sharing the highly sensitive electronic health record (EHR) data with others. However, there exist two main challenges, namely data quality discrepancy, and heterogeneous data distribution, which seriously restrict the development of federated dynamic treatment regimes. To this end, we develop a global data quality aware dynamic treatment regime based on hierarchical federated reinforcement learning across different hospitals. In detail, we first quantify data quality in EHR using immediate health status changes, which are then utilized as rewards to encourage the high-quality treatment actions in the offline actor-critic reinforcement learning model. Within the parameter server, an online reinforcement learning based clustering scheme is proposed to capture the internal similarities to augment the positive knowledge transfer of high-quality hospitals while neglecting the heterogeneity. Extensive experiments are conducted on two diverse real-world datasets to show the advantages of DFR-DTR over state-of-the-art baselines.

In this paper, we propose an adaptive learning paradigm for resource-constrained cross-device federated learning, in which heterogeneous local submodels with varying resources can be jointly trained to produce a global model. Different from existing studies, the submodel structures of different clients are formed by arbitrarily assigned neurons according to their local resources. Along this line, we first design a general resource-adaptive federated learning algorithm, namely RA-Fed, and rigorously prove its convergence with asymptotically optimal rate O(1/√Γ*TQ) under loose assumptions. Furthermore, to address both submodels heterogeneity and data heterogeneity challenges under non-uniform training, we come up with a new server aggregation mechanism RAM-Fed with the same theoretically proved convergence rate. Moreover, we shed light on several key factors impacting convergence, such as minimum coverage rate, data heterogeneity level, submodel induced noises. Finally, we conduct extensive experiments on two types of tasks with three widely used datasets under different experimental settings. Compared with the state-of-the-arts, our methods improve the accuracy up to 10% on average. Particularly, when submodels jointly train with 50% parameters, RAM-Fed achieves comparable accuracy to FedAvg trained with the full model.

With the raised privacy concerns and rigorous data regulations, federated learning has become a hot collaborative learning paradigm for the recommendation model without sharing the highly sensitive POI data. However, the time-sensitive, heterogeneous, and limited POI records seriously restrict the development of federated POI recommendation. To this end, in this paper, we design the fine-grained preference-aware personalized federated POI recommendation framework, namely PrefFedPOI, under extremely sparse historical trajectories to address the above challenges. In details, PrefFedPOI extracts the fine-grained preference of current time slot by combining historical recent preferences and periodic preferences within each local client. Due to the extreme lack of POI data in some time slots, a data amount aware selective strategy is designed for model parameters uploading. Moreover, a performance enhanced clustering mechanism with reinforcement learning is proposed to capture the preference relatedness among all clients to encourage the positive knowledge sharing. Furthermore, a clustering teacher network is designed for improving efficiency by clustering guidance. Extensive experiments are conducted on two diverse real-world datasets to demonstrate the effectiveness of proposed PrefFedPOI comparing with state-of-the-arts. In particular, personalized PrefFedPOI can achieve 7% accuracy improvement on average among data-sparsity clients.

Clinical risk prediction based on Electronic Health Records (EHR) can assist doctors in better judgment and can make sense of early diagnosis. However, the prediction performance heavily relies on effective representations from multi-dimensional time-series EHR data. Existing solutions usually focus on temporal features or inherent relations between clinical event variables or extract both information in two separate phases. This usually leads to insufficient patient feature information and results in poor prediction performance. Moreover, existing methods based on Heterogeneous Graph Neural Network usually require manual selection of proper Meta-Paths. To solve these problems, we propose the Time-aware Context-Gated Graph Attention Network (T-ContextGGAN). Specifically, we design a GNN based module with Time-aware Meta-Paths and self-attention mechanism to extract both temporal semantic information and inherent relations of EHR data simultaneously and perform automatic Meta-Path selection. To evaluate the proposed model, we extract the first 48 hour EHR data in the first Intensive Care Unit (ICU) admission of three different tasks from two open-source datasets and model various clinical variables on the proposed EHRGraph. Extensive experimental results show the proposed model can effectively extract informative features, and outperform existing state-of-art models in terms of various prediction measures. Our code is available in https://github.com/OwlCitizen/TContext-GGAN.

With the increasing prevalence of portable devices and the popularity of community Question Answering (cQA) sites, users can seamlessly post and answer many questions. To effectively organize the information for precise recommendation and easy searching, these platforms require users to select topics for their raised questions. However, due to the limited experience, certain users fail to select appropriate topics for their questions. Thereby, automatic question tagging becomes an urgent and vital problem for the cQA sites, yet it is non-trivial due to the following challenges. On the one hand, vast and meaningful topics are available yet not utilized in the cQA sites; how to model and tag them to relevant questions is a highly challenging problem. On the other hand, related topics in the cQA sites may be organized into a directed acyclic graph. In light of this, how to exploit relations among topics to enhance their representations is critical. To settle these challenges, we devise a graph-guided topic ranking model to tag questions in the cQA sites appropriately. In particular, we first design a topic information fusion module to learn the topic representation by jointly considering the name and description of the topic. Afterwards, regarding the special structure of topics, we propose an information propagation module to enhance the topic representation. As the comprehension of questions plays a vital role in question tagging, we design a multi-level context-modeling-based question encoder to obtain the enhanced question representation. Moreover, we introduce an interaction module to extract topic-aware question information and capture the interactive information between questions and topics. Finally, we utilize the interactive information to estimate the ranking scores for topics. Extensive experiments on three Chinese cQA datasets have demonstrated that our proposed model outperforms several state-of-the-art competitors.

User response prediction, which aims to predict the probability that a user will provide a predefined positive response in a given context such as clicking on an ad or purchasing an item, is crucial to many industrial applications such as online advertising, recommender systems, and search ranking. For these tasks and many other machine learning tasks, an indispensable part of success is feature engineering, where cross features are a significant type of feature transformations. However, due to the high dimensionality and super sparsity of the data collected in these tasks, handcrafting cross features is inevitably time expensive. Prior studies in predicting user response leveraged the feature interactions by enhancing feature vectors with products of features to model second-order or high-order cross features, either explicitly or implicitly. However, these existing methods can be hindered by not learning sufficient cross features due to model architecture limitations or modeling all high-order feature interactions with equal weights. Different features should contribute differently to the prediction, and not all cross features are with the same prediction power.

Given its importance, crime prediction has attracted a lot of attention in the literature, and several methods have been proposed to discover different aspects of characteristics for crime prediction. In this paper, we propose a Clustered Continuous Conditional Random Field (Clustered-CCRF) model which is able to effectively exploit both spatial and temporal factors for crime prediction in an integrated way. In particular, we observe that the crime number at one specific area is not only conditioned on its own historical records but also has high correlation to crime records from similar areas. Therefore, we propose two factors: an auto-regressed temporal correlation and a feature-based inter-area spatial correlation, to measure such patterns for crime prediction. Further, we present a tree-structured clustering algorithm to discover high similar areas based on spatial characteristics to improve the performance of our proposed model. Experiments on real-world crime dataset demonstrate the superiority of our proposed model over the state-of-the-art methods.