FedDBL: Communication and Data Efficient Federated Deep-Broad Learning for Histopathological Tissue Classification

Histopathological tissue classification is a fundamental task in computational pathology. Deep learning-based models have achieved superior performance but centralized training with data centralization suffers from the privacy leakage problem. Federated learning (FL) can safeguard privacy by keeping training samples locally, but existing FL-based frameworks require a large number of well-annotated training samples and numerous rounds of communication which hinder their practicability in the real-world clinical scenario. In this paper, we propose a universal and lightweight federated learning framework, named Federated Deep-Broad Learning (FedDBL), to achieve superior classification performance with limited training samples and only one-round communication. By simply associating a pre-trained deep learning feature extractor, a fast and lightweight broad learning inference system and a classical federated aggregation approach, FedDBL can dramatically reduce data dependency and improve communication efficiency. Five-fold cross-validation demonstrates that FedDBL greatly outperforms the competitors with only one-round communication and limited training samples, while it even achieves comparable performance with the ones under multiple-round communications. Furthermore, due to the lightweight design and one-round communication, FedDBL reduces the communication burden from 4.6GB to only 276.5KB per client using the ResNet-50 backbone at 50-round training. Since no data or deep model sharing across different clients, the privacy issue is well-solved and the model security is guaranteed with no model inversion attack risk. Code is available at https://github.com/tianpeng-deng/FedDBL.

Author as Character and Narrator: Deconstructing Personal Narratives from the r/AmITheAsshole Reddit Community

In the r/AmITheAsshole subreddit, people anonymously share first person narratives that contain some moral dilemma or conflict and ask the community to judge who is at fault (i.e., who is "the asshole"). In general, first person narratives are a unique storytelling domain where the author is the narrator (the person telling the story) but can also be a character (the person living the story) and, thus, the author has two distinct voices presented in the story. In this study, we identify linguistic and narrative features associated with the author as the character or as a narrator. We use these features to answer the following questions: (1) what makes an asshole character and (2) what makes an asshole narrator? We extract both Author-as-Character features (e.g., demographics, narrative event chain, and emotional arc) and Author-as-Narrator features (i.e., the style and emotion of the story as a whole) in order to identify which aspects of the narrative are correlated with the final moral judgment. Our work shows that "assholes" as Characters frame themselves as lacking agency with a more positive personal arc, while "assholes" as Narrators will tell emotional and opinionated stories.

Multi-Reference Image Super-Resolution: A Posterior Fusion Approach

Reference-based Super-resolution (RefSR) approaches have recently been proposed to overcome the ill-posed problem of image super-resolution by providing additional information from a high-resolution image. Multi-reference super-resolution extends this approach by allowing more information to be incorporated. This paper proposes a 2-step-weighting posterior fusion approach to combine the outputs of RefSR models with multiple references. Extensive experiments on the CUFED5 dataset demonstrate that the proposed methods can be applied to various state-of-the-art RefSR models to get a consistent improvement in image quality.

Topological Regularization for Graph Neural Networks Augmentation

The complexity and non-Euclidean structure of graph data hinder the development of data augmentation methods similar to those in computer vision. In this paper, we propose a feature augmentation method for graph nodes based on topological regularization, in which topological structure information is introduced into end-to-end model. Specifically, we first obtain topology embedding of nodes through unsupervised representation learning method based on random walk. Then, the topological embedding as additional features and the original node features are input into a dual graph neural network for propagation, and two different high-order neighborhood representations of nodes are obtained. On this basis, we propose a regularization technique to bridge the differences between the two different node representations, eliminate the adverse effects caused by the topological features of graphs directly used, and greatly improve the performance. We have carried out extensive experiments on a large number of datasets to prove the effectiveness of our model.