The drug recommendation task aims to provide effective and safe prescription decision support for clinical treatment based on patients' past Electronic Health Records (EHR). However, the prevalent phenomenon of missing views in multi-source heterogeneous EHR data may cause performance degradation. This is due to the lack of sufficient information and increased learning difficulties, which limit the practical effectiveness of drug recommendation models in medical applications. In this paper, we emphasize the problems of incompleteness in practical drug recommendation and propose the Incomplete Multi-View Drug Recommendation model via Multi-Level Representation Learning and Curriculum Learning named IMDR. In particular, IMDR employs a Multi-Level Representation Learning architecture equipped with a Medical Code-Level Drug Knowledge Infusion Module and a Visit-Level Cross-View Information Module for patient representation learning to overcome the information loss caused by incomplete data. And then, a Gaussian-guided curriculum learning strategy is proposed to assist the learning process of IMDR with a novel difficulty measure to achieve effective progressive learning under missing medical views. Systematic evaluation on two large-scale real-world medical datasets, MIMIC-III and MIMIC-IV, demonstrates that IMDR reduces the Drug-Drug Interaction (DDI) rate by 2.97% compared to existing state-of-the-art drug recommendation baselines, while achieving significant improvements of 3.29% and 1.97% in Jaccard similarity scores and F1 score, respectively. Furthermore, compared to advanced incomplete multi-view learning (IML) models, IMDR's advantages in Jaccard similarity scores and F1 score further expand to 4.03% and 2.41%.

Federated learning (FL) is a rapidly evolving paradigm that facilitates distributed training of large-scale deep neural networks (DNNs). However, the distributed nature exposes the system to threats from potentially malicious or low-quality participants, which can significantly degrade the overall performance of FL. Existing contribution evaluation approaches in previous FL studies are vulnerable when there exist complicated types of malicious or low-quality clients. In this article, we propose to assess the clients’ contributions by treating their model parameters as data. By extracting information from the statistical properties of model parameters using principal component analysis-based data mining techniques, we quantitatively estimate the similarity and diversity between different clients. Furthermore, we analyze the convergence of our proposed method and establish a convergence rate of$\mathcal {O}({1}/{T})$with commonly accepted assumptions. Extensive experiments are conducted on public datasets to evaluate the effectiveness of our proposed method against typical malicious or low-quality clients: sybil-based backdoor attackers and clients with redundant data. Experimental results demonstrate the superiority of our approach in excluding malicious or low-quality clients and thereby enhancing the model performance in FL.

In recent years, the integration of federated learning and deep learning technologies has become increasingly prevalent in privacy-preserved scenarios, such as smart health applications and automatic financial support. However, the inherent robustness issue in deep learning poses potential risks to federated learning systems when subjected to various attack methods. These attacks can inflict damage during the training and testing phases, perturbing models and inputs. To enhance the robustness of existing federated learning systems, we propose a novel framework called RobFL. This framework incorporates a unique feature learning module - feature center separation learning - that is specifically designed to increase the margins between different classes in the feature space, thereby augmenting the difficulty of attacks employing imperceptible perturbations on inputs. Furthermore, we design a malicious center detection method to detect malicious clients and mitigate their adverse impact. Extensive experiments substantiate the robustness of our proposed framework, RobFL, demonstrating its resilience against both evasion attacks and poisoning attacks.

In the Industrial Internet of Things (IIoT), cross-silo federated learning (CSFL) enables entities, such as manufacturers and suppliers to train global models for optimizing production processes while ensuring data privacy. A well-designed incentive mechanism is essential to persuade clients to contribute data resources. However, existing methodologies overlook the dynamic nature of the training process, where the accuracy of the globally trained model and the client’s data ownership change over time. Furthermore, the majority of previous research assumes a defined functional relationship between the data contribution and the model accuracy, which is infeasible in realistic and dynamic training environments. To address these challenges, we design a novel adaptive mechanism for CSFL that inspires organizations to contribute data resources in a dynamic training environment with the aim of maximizing their long-term payoffs. This mechanism leverages multiagent reinforcement learning (MARL) to ascertain near-optimal data contribution strategies from potential game histories without necessitating private organizational information or a precise accuracy function. Experimental results indicate that our mechanism achieves adaptive incentive in dynamic environments and effectively enhances the long-term payoffs of organizations.

Digital advertising constitutes one of the main revenue sources for online platforms. In recent years, some advertisers tend to adopt auto-bidding tools to facilitate advertising performance optimization, making the classical utility maximizer model in auction theory not fit well. Some recent studies proposed a new model, called value maximizer, for auto-bidding advertisers with return-on-investment (ROI) constraints. However, the model of either utility maximizer or value maximizer could only characterize partial advertisers in real-world advertising platforms. In a mixed environment where utility maximizers and value maximizers coexist, the truthful ad auction design would be challenging since bidders could manipulate both their values and affiliated classes, leading to a multi-parameter mechanism design problem. In this work, we address this issue by proposing a payment rule which combines the corresponding ones in classical VCG and GSP mechanisms in a novel way. Based on this payment rule, we propose a truthful auction mechanism with an approximation ratio of 2 on social welfare, which is close to the lower bound of at least 5/4 that we also prove. The designed auction mechanism is a generalization of VCG for utility maximizers and GSP for value maximizers.

Cross-silo federated learning (CFL) is a distributed learning paradigm that allows organizations (e.g., financial or medical entities) to train a global model on siloed data. Recent studies on mechanisms designed for CFL, however, rarely jointly consider the potential inter-organizational competition and the lack of credibility between organizations, which may discourage organizational participation. In this paper, we investigate the problem of inter-organizational competition and credibility assurance. We propose a distributed trading mechanism, called$TradeFL$, to incentivize organizations to contribute data and computational resources through mutual trading among organizations. Technically, TradeFL characterizes the competition among organizations and compensates for their damage incurred by competition. TradeFL runs on distributed organizations and provides credibility guarantees for compensation through a customized smart contract11Illustration of the prototype: https://github.com/user10963.. We prove that the interaction among organizations that contribute resources to maximize personal payoffs is a weighted potential game. Then, we propose a centralized algorithm and a distributed algorithm to determine the optimal resource contribution. Simulation results and evaluations based on real-world datasets demonstrate that our scheme achieves higher social welfare, increases the amount of contributed data by up to 64%, and improves the accuracy of the global model by at most 23.2%.

Federated Learning (FL) is a distributed machine learning technique that trains models on local devices to preserve data privacy. In FL, evaluating model quality is crucial for detecting malicious clients and improving model accuracy. However, existing methods typically require a representative public testing dataset on the server, which is often unavailable in practical federated learning scenarios. To address this problem, we propose a novel four-step framework, taking a crowdsourcing approach. The basic idea is to distribute the model to be evaluated as a task to a set of testing clients selected from the original clients pool, who evaluate the model quality using their local datasets. By consolidating these individual evaluations, we obtain the overall model quality. To select a suitable number of testing clients, we propose an exploration-exploitation-based framework. Furthermore, to safeguard against attacks from potential malicious testing clients, we introduce a Correlated Agreement (CA) mechanism. This is achieved by comparing correlations of accuracy among the same set of testing clients (who were selected for the aforementioned evaluation task). Extensive experiments demonstrate the effectiveness of our approach, which yields accuracy comparable to methods that rely on a public testing dataset on the server. Moreover, our approach can identify and filter out dishonest testing clients and thereby ensure model quality even in adversarial settings.

In e-commerce advertising, it is crucial to jointly consider various performance metrics, e.g., user experience, advertiser utility, and platform revenue. Traditional auction mechanisms, such as GSP and VCG auctions, can be suboptimal due to their fixed allocation rules to optimize a single performance metric (e.g., revenue or social welfare). Recently, data-driven auctions, learned directly from auction outcomes to optimize multiple performance metrics, have attracted increasing research interests. However, the procedure of auction mechanisms involves various discrete calculation operations, making it challenging to be compatible with continuous optimization pipelines in machine learning. In this paper, we design Deep Neural Auctions (DNAs) to enable end-to-end auction learning by proposing a differentiable model to relax the discrete sorting operation, a key component in auctions. We optimize the performance metrics by developing deep models to efficiently extract contexts from auctions, providing rich features for auction design. We further integrate the game theoretical conditions within the model design, to guarantee the stability of the auctions. DNAs have been successfully deployed in the e-commerce advertising system at Taobao. Experimental evaluation results on both large-scale data set as well as online A/B test demonstrated that DNAs significantly outperformed other mechanisms widely adopted in industry.

Federated learning (FL) trains a machine learning model on mobile devices in a distributed manner using each device’s private data and computing resources. A critical issues is to evaluate individual users’ contributions so that (1) users’ effort in model training can be compensated with proper incentives and (2) malicious and low-quality users can be detected and removed. The state-of-the-art solutions require a representative test dataset for the evaluation purpose, but such a dataset is often unavailable and hard to synthesize. In this paper, we propose a method called Pairwise Correlated Agreement (PCA) based on the idea of peer prediction to evaluate user contribution in FL without a test dataset. PCA achieves this using the statistical correlation of the model parameters uploaded by users. We then apply PCA to designing (1) a new federated learning algorithm called Fed-PCA, and (2) a new incentive mechanism that guarantees truthfulness. We evaluate the performance of PCA and Fed-PCA using the MNIST dataset and a large industrial product recommendation dataset. The results demonstrate that our Fed-PCA outperforms the canonical FedAvg algorithm and other baseline methods in accuracy, and at the same time, PCA effectively incentivizes users to behave truthfully.

Real-time information processing is critical to the success of diverse applications from many areas. Age of Information (AoI), as a new metric, has received considerable attention to evaluate the performance of real-time information processing systems. In recent years, edge computing is becoming an efficient paradigm to reduce the AoI and to provide the real-time services. Considering the substantial deployment cost and the resulting resource limitation in edge computing, a proper pricing mechanism is highly necessary to fully utilize edge resources and then minimize the overall AoI of the whole system. However, there are two challenges to design this mechanism: 1) the priorities (or values) of the real-time computing tasks, critical to the efficient resource allocation, are usually private information of users and may be manipulated by selfish users for their own interests; 2) due to the time-varying property of AoI, the values of the tasks discount with time, making the traditional pricing mechanisms infeasible. In this paper, we extend the classical Myerson Theorem to the online setting with time discounting tasks values, and accordingly propose an online auction mechanism, called PreDisc, including an allocation rule and a payment rule. We leverage dynamic programming to greedily allocate resources in each time slot, and charge the winning user with a new critical price, extended from the classical Myerson payment rule. A preemption factor is further employed to make a trade-off between the newly arrived tasks and ongoing tasks. We prove that PreDisc guarantees the economic property of strategy-proofness and achieves a constant competitive ratio. We conduct extensive simulations and the results demonstrate that PreDisc outperforms the traditional mechanisms, in terms of both weighted AoI and revenue of edge service providers. Compared with the optimal solution in offline VCG mechanism, PreDisc has much lower computation complexity with only a slight performance loss.

Bike sharing systems have been widely deployed around the world in recent years. A core problem in such systems is to reposition the bikes so that the distribution of bike supply is reshaped to better match the dynamic bike demand. When the bike-sharing company or platform is able to predict the revenue of each reposition task based on historic data, an additional constraint is to cap the payment for each task below its predicted revenue. In this paper, we propose an incentive mechanism called TruPreTar to incentivize users to park bicycles at locations desired by the platform toward rebalancing supply and demand. TruPreTar possesses four important economic and computational properties such as truthfulness and budget feasibility. Furthermore, we prove that even when the payment budget is tight, the total revenue still exceeds or equals the budget. Otherwise, TruPreTar achieves 2-approximation as compared to the optimal (revenue-maximizing) solution, which is close to the lower bound of at least √2 that we also prove. Using an industrial dataset obtained from a large bike-sharing company, our experiments show that TruPreTar is effective in rebalancing bike supply and demand and, as a result, generates high revenue that outperforms several benchmark mechanisms.