High-quality data labeling is essential for training robust machine learning models; however, existing methods often ignore fraud or assume non-negative worker utility, failing to penalize harmful contributions without discouraging participation. To address this, we propose the Negativity-Aware Incentive (NAI) mechanism which introduces two novel components. First, the Ability-Result Characteristic Function (AR-CF) adapts and extends Shapley value theory through signed valuation to explicitly capture both positive and negative contributions, by combining workers' abilities with real-time task results to define contribution values. Second, a dynamic stake pool mechanism employs pre-commitment economics with adaptive dual-control parameters to balance fairness and operational efficiency. Through extensive experiments on multimodal datasets (images, text, audio, video), NAI outperforms state-of-the-art baselines: it improves video labeling accuracy by 16.6%, and reduces fraudulent behaviors by 33.9%. Furthermore, our deployment on the NetEase Youling crowdsourcing platform, serving 430,000 registered workers with 80,000 monthly active workers, validates NAI's real-world viability. Real-time A/B testing shows a 59.6% improvement in labeling quality for beginner tasks and a consistent reduction in fraud rates (14.8%-33.9%) across difficulty levels. This work establishes a paradigm shift in crowdsourcing system design, demonstrating that explicit negative modeling can enhance data quality, optimize costs, and foster participation at scale.

As a promising distributed machine learning paradigm, Federated Learning (FL) has attracted increasing attention to deal with data silo problems without compromising user privacy. By adopting the classic one-to-multi training scheme (i.e., FedAvg), where the cloud server dispatches one single global model to multiple involved clients, conventional FL methods can achieve collaborative model training without data sharing. However, since only one global model cannot always accommodate all the incompatible convergence directions of local models, existing FL approaches greatly suffer from inferior classification accuracy. To address this issue, we present an efficient FL framework named FedCross, which uses a novel multi-to-multi FL training scheme based on our proposed multi-model cross-aggregation approach. Unlike traditional FL methods, in each round of FL training, FedCross uses multiple middleware models to conduct weighted fusion individually. Since the middleware models used by FedCross can quickly converge into the same flat valley in terms of loss landscapes, the generated global model can achieve a well-generalization. Experimental results on various well-known datasets show that, compared with state-of-the-art FL methods, Fed Cross can significantly improve FL accuracy within both IID and non-IID scenarios without causing additional communication overhead.

Graph convolutional networks (GCNs) are promising for graph learning tasks. For privacy-preserving graph learning tasks involving distributed graph datasets, federated learning (FL)-based GCN (FedGCN) training is required. An important open challenge for FedGCN is scaling to large graphs, which typically incurs 1) high computation overhead for handling the explosively-increasing number of neighbors, and 2) high communication overhead of training GCNs involving multiple FL clients. Thus, neighbor sampling is being studied to enhance the scalability of FedGCNs. Existing FedGCN training techniques with neighbor sampling often produce extremely large communication and computation overhead and inaccurate node embeddings, leading to poor model performance. To bridge this gap, we propose the Federated Adaptive Attention-based Sampling (FedAAS) approach. It achieves substantial cost savings by efficiently leveraging historical embedding estimators and focusing the limited communication resources on transmitting the most influential neighbor node embeddings across FL clients. We further design an adaptive embedding synchronization scheme to optimize the efficiency and accuracy of FedAAS on large-scale datasets. Theoretical analysis shows that the approximation error induced by the staleness of historical embedding is upper bounded, and the model is guaranteed to converge in an efficient manner. Extensive experimental evaluation against four state-of-the-art baselines on six real-world graph datasets show that FedAAS achieves up to 5.12% higher test accuracy, while saving communication and computation costs by 95.11% and 94.76%, respectively.

IoT devices provide users with great convenience in smart homes. However, the interdependent behaviors across devices may yield unexpected interactions. To analyze the potential IoT interaction vulnerabilities, in this paper, we propose a federated and explicable IoT interaction data management system FexIoT. To address the lack of information in the closed-source platforms, FexIoT captures causality information by fusing multi-domain data, including the descriptions of apps and real-time event logs, into interaction graphs. The interaction graph representation is encoded by graph neural networks (GNNs). To collaboratively train the GNN model without sharing the raw data, we design a layer-wise clustering-based federated GNN framework for learning intrinsic clustering relationships among GNN model weights, which copes with the statistical heterogeneity and the concept drift problem of graph data. In addition, we propose the Monte Carlo beam search with the SHAP method to search and measure the risk of subgraphs, in order to explain the potential vulnerability causes. We evaluate our prototype on datasets collected from five IoT automation platforms. The results show that FexIoT achieves more than 90% average accuracy for interaction vulnerability detection, outperforming the existing methods. Moreover, FexIoT offers an explainable result for the detected vulnerabilities.

Federated Learning (FL) enables a large number of data owners (a.k.a. FL clients) to jointly train a machine learning model without disclosing private local data. The importance of local data samples to the FL model vary widely. This is exacerbated by the presence of noisy data, which exhibit large losses similar to important (hard) samples. Currently, there lacks an FL approach that can effectively distinguish hard samples (which are beneficial) from noisy samples (which are harmful). To bridge this gap, we propose the Federated Client and Sample Selection (FedCSS) approach. It is a bilevel optimization approach for FL client-and-sample selection to achieve hard sample-aware noise-robust learning in a privacy preserving manner. It performs meta-learning based online approximation to iteratively update global FL models, select the most positively influential samples and deal with training data noise. Theoretical analysis shows that it is guaranteed to converge in an efficient manner. Experimental comparison against six state-of-the-art baselines on five real-world datasets in the presence of data noise and heterogeneity shows that it achieves up to 26.4% higher test accuracy, while saving communication and computation costs by at least 41.5% and 1.2%, respectively.

Federated Learning (FL) enables multiple partici-pants to collaboratively train a model in a privacy-preserving way. The performance of the FL model heavily depends on the quality of participants' local data, which makes measuring the contributions of participants an essential task for various purposes, e.g., participant selection and reward allocation. The Shapley value is widely adopted by previous work for contribution assessment, which, however, requires repeatedly leave-one-out retraining and thus incurs the prohibitive cost for FL. In this paper, we propose a highly efficient approach, named DIG-FL, to estimate the Shapley value of each participant without any model retraining. It's worth noting that our approach is applicable to both vertical federated learning (VFL) and horizontal federated learning (HFL), and we provide concrete design for VFL and HFL. In addition, we propose a DIG-FL based reweight mechanism to improve the model training in terms of accuracy and convergence speed by dynamically adjusting the weights of participants according to their per-epoch contributions, and theoretically analyze the convergence speed. Our extensive evaluations on 14 public datasets show that the estimated Shapley value is very close to the actual Shapley value with Pearson's correlation coefficient up to 0.987, while the cost is orders of magnitude smaller than state-of-the-art methods. When there are more than 80% participants holding low-quality data, by dynamically adjusting the weights, DIG-FL can effectively accelerate the convergence and improve the model accuracy.

In online display advertising, guaranteed delivery (GD) ads and real-time bidding (RTB) are two main ways to sell impressions for a publisher. While RTB has gained increasing popularity, there is still a proportion of revenue generated from GD ads [1]. Existing mainstream impression allocation models deal with the two delivery ways separately, failing to achieve optimal allocation for multi-objective under multi-constraints, e.g., maximizing gross merchandise volume pre mille (GPM) and revenue per mille (RPM), thus limiting the overall revenue for both the publisher and advertisers. To solve the above problems, we propose a unified guaranteed impression allocation framework to optimally allocate impressions for both GD ads and RTB ads simultaneously. Specifically, we formulate the optimization problem as a non-convex quadratically constrained quadratic programming (QCQP) problem. Then we design an end-to-end unified impression allocation framework to approximately solve the QCQP problem. Furthermore, experiments on real data from Tencent News show that our design significantly increases the overall revenue of both the publisher and advertisers, while achieving much faster convergence than the current state-of-the-art methods.

Federated learning (FL) enables large amounts of participants to construct a global learning model, while storing training data privately at each client device. A fundamental issue in this framework is the susceptibility to the erroneous training data. This problem is especially challenging due to the invisibility of clients' local training data and training process, as well as the resource constraints of a large number of mobile and edge devices. In this paper, we try to tackle this challenging issue by introducing the first FL debugging framework, FLDebugger, for mitigating test error caused by erroneous training data. The pro-posed solution traces the global model's bugs (test errors), jointly through the training log and the underlying learning algorithm, back to first identify the clients and subsequently their training samples that are most responsible for the errors. In addition, we devise an influence-based participant selection strategy to fix bugs as well as to accelerate the convergence of model retraining. The performance of the identification algorithm is evaluated via extensive experiments on a real AIoT system (50 clients, including 20 edge computers, 20 laptops and 10 desktops) and in larger-scale simulated environments. The evaluation results attest to that our framework achieves accurate and efficient identification of negatively influential clients and samples, and significantly improves the model performance by fixing bugs.