A state-of-the-art survey on solving non-IID data in Federated Learning

Social recommendation has achieved great success in many domains including e-commerce and location-based social networks. Existing methods usually explore the user-item interactions or user-user co...

HyperSoRec: Exploiting Hyperbolic User and Item Representations with Multiple Aspects for Social-aware Recommendation

Humans’ ability to transfer knowledge through teaching is one of the essential aspects for human intelligence. A human teacher can track the knowledge of students to customize the teaching on students’ needs. With the rise of online education platforms, there is a similar need for machines to track the knowledge of students and tailor their learning experience. This is known as the Knowledge Tracing (KT) problem in the literature. Effectively solving the KT problem would unlock the potential of computer-aided education applications such as intelligent tutoring systems, curriculum learning, and learning materials’ recommendation. Moreover, from a more general viewpoint, a student may represent any kind of intelligent agents including both human and artificial agents. Thus, the potential of KT can be extended to any machine teaching application scenarios which seek for customizing the learning experience for a student agent (i.e., a machine learning model). In this paper, we provide a comprehensive survey for the KT literature. We cover a broad range of methods starting from the early attempts to the recent state-of-the-art methods using deep learning, while highlighting the theoretical aspects of models and the characteristics of benchmark datasets. Besides these, we shed light on key modelling differences between closely related methods and summarize them in an easy-to-understand format. Finally, we discuss current research gaps in the KT literature and possible future research and application directions.

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Knowledge Tracing: A Survey

To avoid the original private data uploading in cloud-edgecomputing, the federated learning (FL) scheme is recently proposed which enhances the privacy preservation. However, the attacks against the uploaded model updates in FL can still cause private data leakage which demotivates the privacy-sensitive participating edge devices. To address this issue, we aim to design a privacy-preserving incentive mechanism for the federated cloud-edge learning (PFCEL) system such that 1) the privacy-sensitive edge devices are motivated to contribute to the local training and model uploading, 2) a trade-off between the private data leakage and the model accuracy is achieved. We first model the data leakage quantitatively from an adversarial perspective, and then formulate the incentive design problem as a three-layer Stackelberg game, where the interaction between the edge servers and edge devices is further formulated as an optimal contract design problem. Extensive theoretical analysis and numerical evaluations demonstrate the effectiveness of our designed mechanism in terms of privacy preservation and system utility.

Privacy-Preserving Incentive Mechanism Design for Federated Cloud-Edge Learning

Learning informative representations for educational questions is a fundamental problem in online learning systems, which can promote many applications, e.g., difficulty estimation. Most solutions integrate all information of one question together following a supervised manner, where the representation results are unsatisfactory sometimes due to the following issues. First, they cannot ensure the presentation ability due to the scarcity of labeled data. Then, the label-dependent representation results have poor feasibility to be transferred. Moreover, aggregating all information into the unified may introduce some noises in applications since it cannot distinguish the diverse characteristics of questions. In this paper, we aim to learn the disentangled representations of questions. We propose a novel unsupervised model, namely DisenQNet, to divide one question into two parts, i.e., a concept representation that captures its explicit concept meaning and an individual representation that preserves its personal characteristics. We achieve this goal via mutual information estimation by proposing three self-supervised estimators in a large unlabeled question corpus. Then, we propose another enhanced model, DisenQNet+, that transfers the representation knowledge from unlabeled questions to labeled questions in specific applications by maximizing the mutual information between both. Extensive experiments on real-world datasets demonstrate that DisenQNet can generate effective and meaningful disentangled representations for questions, and furthermore, DisenQNet+ can improve the performance of different applications.

DisenQNet: Disentangled Representation Learning for Educational Questions

User modeling aims to capture the latent characteristics of users from their behaviors, and is widely applied in numerous applications. Usually, centralized user modeling suffers from the risk of privacy leakage. Instead, federated user modeling expects to provide a secure multi-client collaboration for user modeling through federated learning. Existing federated learning methods are mainly designed for consistent clients, which cannot be directly applied to practical scenarios, where different clients usually store inconsistent user data. Therefore, it is a crucial demand to design an appropriate federated solution that can better adapt to user modeling tasks, and however, meets following critical challenges: 1) Statistical heterogeneity. The distributions of user data in different clients are not always independently identically distributed which leads to personalized clients; 2) Privacy heterogeneity. User data contains both public and private information, which have different levels of privacy. It means we should balance different information to be shared and protected; 3) Model heterogeneity. The local user models trained with client records are heterogeneous which need flexible aggregation in the server. In this paper, we propose a novel client-server architecture framework, namely Hierarchical Personalized Federated Learning (HPFL) to serve federated learning in user modeling with inconsistent clients. In the framework, we first define hierarchical information to finely partition the data with privacy heterogeneity. On this basis, the client trains a user model which contains different components designed for hierarchical information. Moreover, client processes a fine-grained personalized update strategy to update personalized user model for statistical heterogeneity. Correspondingly, the server completes a differentiated component aggregation strategy to flexibly aggregate heterogeneous user models in the case of privacy and model heterogeneity. Finally, we conduct extensive experiments on real-world datasets, which demonstrate the effectiveness of the HPFL framework.

Hierarchical Personalized Federated Learning for User Modeling

The contexts and cultures have a direct impact on student learning by affecting student's implicit cognitive states, such as the preference and the proficiency on specific knowledge. Motivated by the success of context-aware modeling in various fields, such as recommender systems, in this paper, we propose to study how to model context-aware features and adapt them for more precisely diagnosing student's knowledge proficiency. Specifically, by analyzing the characteristics of educational contexts, we design a two-stage framework ECD (Educational context-aware Cognitive Diagnosis), where a hierarchical attentive network is first proposed to represent the context impact on students and then an adaptive optimization is used to achieve diagnosis enhancement by aggregating the cognitive states reflected from both educational contexts and students' historical learning records. Moreover, we give three implementations of general ECD framework following the typical cognitive diagnosis solutions. Finally, we conduct extensive experiments on nearly 52 million records of the students sampled by PISA (Programme for International Student Assessment) from 73 countries and regions. The experimental results not only prove that ECD is more effective in student performance prediction since it can well capture the impact from educational contexts to students' cognitive states, but also give some interesting discoveries regarding the difference among different educational contexts in different countries and regions.

Modeling Context-aware Features for Cognitive Diagnosis in Student Learning

Automatically answering mathematical problems is a challenging task since it requires not only the ability of linguistic understanding but also mathematical comprehension. Existing studies usually explore solutions on the elementary math word problems that aim to understand the questions described in natural language narratives, which are not capable of solving more general problems containing structural formulas. To this end, in this paper, we propose a novel Neural Mathematical Solver (NMS) with enhanced formula structures. Specifically, we first frame the formulas in a certain problem as a TeX dependency graph to preserve formula-enriched structures. Then, we design a formula graph network (FGN) to capture its mathematical relations. Next, we develop a novel architecture with two GRU models, connecting tokens from both word space and formula space together, to learn the linguistic semantics for the answers. Extensive experiments on a large-scale dataset demonstrate that NMS not only achieves better answer prediction but also visualizes reasonable mathematical representations of problems.

Neural Mathematical Solver with Enhanced Formula Structure

Knowledge Tracing (KT), namely tracking the knowledge conditions of each student across time, has always been challenging due to the latent and time-varying characteristics of knowledge states. Traditional psychometrical frameworks lack the ability to extract rich representations of exercises or examinees. Deep learning KT models have shown comparable performances, but uninterpretable model parameters have limited their applications. Furthermore, existing frameworks usually cannot handle temporal factors appropriately, as most of them simply apply stochastic processes to simulate fluctuations on knowledge states over time. In this paper, we propose a new framework named Time-and-Concept Enhanced Deep Multidimensional Item Response Theory (TC-MIRT) that integrates the parameters of a Multidimensional Item Response Theory into an improved recurrent neural network. Specifically, two enhanced components are constructed to empower our model with the ability to perform trend forecasting and to generate explainable parameters in each specific domain of knowledge. Experiments implemented on two real-world datasets show that our framework outperforms state-of-the-art KT approaches on performance prediction tasks. Moreover, extensive case analyses also demonstrate that the interpretable parameters of TC-MIRT can be used to evaluate the strengths and weaknesses of students.

Time-and-Concept Enhanced Deep Multidimensional Item Response Theory for interpretable Knowledge Tracing

Knowledge tracing is a fundamental task in intelligent education for tracking the knowledge states of students on necessary concepts. In recent years, Deep Knowledge Tracing (DKT) utilizes recurrent neural networks to model student learning sequences. This approach has achieved significant success and has been widely used in many educational applications. However, in practical scenarios, it tends to suffer from the following critical problems due to data isolation: 1) Data scarcity. Educational data, which is usually distributed across different silos (e.g., schools), is difficult to gather. 2) Different data quality. Students in different silos have different learning schedules, which results in unbalanced learning records, meaning that it is necessary to evaluate the learning data quality independently for different silos. 3) Data incomparability. It is difficult to compare the knowledge states of students with different learning processes from different silos. Inspired by federated learning, in this paper, we propose a novel Federated Deep Knowledge Tracing (FDKT) framework to collectively train high-quality DKT models for multiple silos. In this framework, each client takes charge of training a distributed DKT model and evaluating data quality by leveraging its own local data, while a center server is responsible for aggregating models and updating the parameters for all the clients. In particular, in the client part, we evaluate data quality incorporating different education measurement theories, and we construct two quality-oriented implementations based on FDKT, i.e., FDKTCTT and FDKTIRT-where the means of data quality evaluation follow Classical Test Theory and Item Response Theory, respectively. Moreover, in the server part, we adopt hierarchical model interpolation to uptake local effects for model personalization. Extensive experiments on real-world datasets demonstrate the effectiveness and superiority of the FDKT framework.

Jinze Wu

Papers

Hierarchical Personalized Federated Learning for User Modeling

Modeling Context-aware Features for Cognitive Diagnosis in Student Learning

Neural Mathematical Solver with Enhanced Formula Structure

Time-and-Concept Enhanced Deep Multidimensional Item Response Theory for interpretable Knowledge Tracing

Federated Deep Knowledge Tracing