Acquiring big-size datasets to raise the performance of deep models has become one of the most critical problems in representation learning (RL) techniques, which is the core potential of the emerging paradigm of federated learning (FL). However, most current FL models concentrate on seeking an identical model for isolated clients and thus fail to make full use of the data specificity between clients. To enhance the classification performance of each client, this study introduces the FDRL, a federated discriminative RL model, by partitioning the data features of each client into a global subspace and a local subspace. More specifically, FDRL learns the global representation for federated communication between those isolated clients, which is to capture common features from all protected datasets via model sharing, and local representations for personalization in each client, which is to preserve specific features of clients via model differentiating. Toward this goal, FDRL in each client trains a shared submodel for federated communication and, meanwhile, a not-shared submodel for locality preservation, in which the two models partition client-feature space by maximizing their differences, followed by a linear model fed with combined features for image classification. The proposed model is implemented with neural networks and optimized in an iterative manner between the server of computing the global model and the clients of learning the local classifiers. Thanks to the powerful capability of local feature preservation, FDRL leads to more discriminative data representations than the compared FL models. Experimental results on public datasets demonstrate that our FDRL benefits from the subspace partition and achieves better performance on federated image classification than the state-of-the-art FL models.

Federated learning (FL) allows a large number of clients to improve their respective models through training a shared global model. However, passing the same global model is not conducive to the training of a few clients and leads to a large loss of localization information. In practical, there are often some prior information that can be shared between clients. Our study takes into account the use of such prior information to calculate a personalized global model for each client, resulting in an enhanced personalized federated learning framework, dubbed PerFL for short, that takes advantage of available client features that can be shared with other clients. More specifically, PerFL calculates the incidence matrix of all involved clients by using the permitted shareable side information and then updates the local models by using their similar clients instead of all clients. Employing the neural network as the classification model, PerFL learns the parameter matrices at each client in an iterative manner. On three publicly available image datasets, PerFL can benefit from the employed similarity and achieve an improved classification performance in comparison with the state-of-the-art FL models.

This paper studies the problem of developing contrastive learning into the privacy-protected federated learning (FL), which is to achieve more data samples for model training. The existing methods usually encourage the global model and local models in FL to be the same one, often ignoring the data heterogeneity of the clients. In this paper, we proposed a method of personalized federated contrastive learning to improve the FL model performance for each client’s task, by learning a global representation and a local representation simultaneously. Our method is a novel FL framework that borrows the scheme of contrastive learning (CL), where one CL branch is the global model while the other branch is the local model divided into a share part and a personalized part. The proposed model is then trained by maximizing the agreement between the global model and the sharing part of the local model and meanwhile minimizing the agreement between the global model and the personalized part. We conducted evaluations on three public datasets for federated image classification. The results show that the proposed method can benefit from the personalization of local models and thus achieve better accuracy in comparison with the state-of-the-art FL models.

Recommendations in the educational environment aim to help learner access their personalized demands efficiently. Unlike commodity recommendation, limited to the ethics of pedagogy and the high cost of bad recommendations, the credibility and interpretability of the education recommendation system are more worthy of attention to achieve recommendation accuracy. However, few studies focused on the interpretability of recommendations. Thus, this study proposes an Open Recommendation framework for Interpretability based on the Bayesian principal component analysis (PPCA), ORec4Int. ORec4Int helps learners understand the recommendation by building a mapping between educational resources and the latent factors/features of learners. The interpretability will enhance his/her trust in the education recommendation system. Finally, We not only evaluate the recommendation performance of ORec4Int based on one real-world dataset but also compared its performance in interpretability and the education expert solution. Results show that ORec4Int can approach the performance of education expert solutions. Ultimately, ORec4Int is faster, more efficient, and less costly.