Recently, Heterogeneous Federated Learning (HtFL) has attracted attention due to its ability to support heterogeneous models and data. To reduce the high communication cost of transmitting model parameters, a major challenge in HtFL, prototype-based HtFL methods are proposed to solely share class representatives, a.k.a, prototypes, among heterogeneous clients while maintaining the privacy of clients’ models. However, these prototypes are naively aggregated into global prototypes on the server using weighted averaging, resulting in suboptimal global knowledge which negatively impacts the performance of clients. To overcome this challenge, we introduce a novel HtFL approach called FedTGP, which leverages our Adaptive-margin-enhanced Contrastive Learning (ACL) to learn Trainable Global Prototypes (TGP) on the server. By incorporating ACL, our approach enhances prototype separability while preserving semantic meaning. Extensive experiments with twelve heterogeneous models demonstrate that our FedTGP surpasses state-of-the-art methods by up to 9.08% in accuracy while maintaining the communication and privacy advantages of prototype-based HtFL. Our code is available at https://github.com/TsingZ0/FedTGP.

With the wide application of recommender systems, the potential impacts of recommender systems on customers, item providers and other parties have attracted increasing attention. Fairness, which is the quality of treating people equally, is also becoming important in recommender system evaluation and algorithm design. Therefore, in the past years, there has been a growing interest in fairness measurement and assurance in recommender systems. Although there are several reviews on related topics, such as fairness in machine learning and debias in recommender systems, they do not present a systematic view on fairness in recommender systems, which is context aware and has a multi-sided meaning. Therefore, in this review, the concept of fairness is discussed in detail in the various contexts of recommender systems. Specifically, a comprehensive framework to classify fairness metrics is proposed from four dimensions, i.e.,Fairness for Whom,Demographic Unit,Time Frame, andQuantification Method. Then the strategies for eliminating unfairness in recommendations, fairness in different recommendation tasks and datasets are reviewed and summarized. Finally, the challenges and future work are discussed.

Agent-based service composition has provided a promising computing paradigm for the automatic web service composition. In this paper, a formal service agent model is proposed, which integrates the web service and software agent technologies into one cohesive entity. Based on the service agent model, a distributed planning algorithm for web service composition called DPAWSC is presented. DPAWSC formalizes web service composition into a graph search problem according to the dependence relations among service agents. The key to DPAWSC is that the alternative solution with smaller length has higher priority to be searched than one with larger length. DPAWSC is based on the distributed decision making of the autonomous service agents and addresses the distributed nature of web service composition. We evaluate the algorithm by simulation experiments and the results demonstrate that DPAWSC is effective for its ability to produce the high quality solution at a low cost of communications.

In process-aware information systems (PAISs), anomalies are ubiquitous, having a number of different underlying causes, such as software malfunctions or operator errors. The presence of anomalies not only has an enormous impact on the economic well-being of the business, but also interferes with our ability to mine useful information from event logs. In this paper, we propose GRASPED, a GR U- A E Network based multi-per S pective business P ROC E ss anomaly D etection model. GRASPED can detect anomalies not only from the control flow but also from the data perspective of a business process. GRASPED is based on an autoencoder (AE) with gated recurrent units (GRU) which is trained in an unsupervised fashion (i.e., does not require any labeling of the data). In addition, GRASPED introduces the teacher forcing method as well as the attention mechanism to improve its detection performance. GRASPED does not require training on a clean log (i.e., it can be trained and perform anomaly detection directly on logs containing anomalies). We conduct extensive experiments on synthetic logs as well as real-life logs. The experiment results show that GRASPED outperforms the state-of-the-art methods for both trace-level and attribute-level anomaly detection.