Federated Learning (FL), as an emerging distributed learning paradigm to protect data privacy, facilitates model training collaborations among entities. However, performance of FL model in such a promising paradigm would drop dramatically without adequate participating entities' data. To maintain sustainability and efficiency of FL system, it is inevitable to design a reasonable and fair contribution estimation method. Despite of success, existing studies of contribution estimation do not adequately balance efficiency and fairness. To overcome such a limitation, we propose two plug-and-play methods, named as vector projection method (VPM) and income exclusion method (IEM), to estimate the contribution of each entity in FL. As standalone contribution measurement modules, they can be deployed to almost any FL server flexibly. We conduct extensive experiments on widely-used Fashion-MNIST and CIFAR-10 datasets. The experimental results demonstrate the promising effectiveness of contribution estimation, and show the convergence acceleration under contribution-weighted aggregation mechanism.

Multimodal Machine Comprehension ($\rm M^3C$) has been a challenging task that requires understanding both language and vision, as well as their integration and interaction. For example, the RecipeQA challenge, which provides several $\rm M^3C$ tasks, requires deep neural models to understand textual instructions, images of different steps, as well as the logic orders of food cooking. To address this challenge, we propose a Multi-Level Multi-Modal Transformer (MLMM-Trans) framework to integrate and understand multiple textual instructions and multiple images. Our model can conduct intensive attention mechanism at multiple levels of objects (e.g., step level and passage-image level) for sequences of different modalities. Experiments have shown that our model can achieve the state-of-the-art results on the three multimodal tasks of RecipeQA.

Multi-Choice Reading Comprehension~(MCRC) is an essential task where a machine selects the correct answer from multiple choices given a context document and a corresponding question. Existing methods usually make predictions based on a single-round reasoning process with the attention mechanism, however, this may be insufficient for tasks that require a more complex reasoning process. To effectively comprehend the context and select the correct answer from different perspectives, we propose the Read-Attend-Exclude (RAE) model which is motivated by what human readers do for MCRC in multi-rounds reasoning process. Specifically, the RAE model includes four components: the Scan Reading Module, the Attended Intensive Reading Module, the Answer Exclusion Module, and the Gated Fusion Module that makes the final decisions collectively based on the aforementioned three modules. Extensive experiments demonstrate the strong results of the proposed model on the DREAM dataset and the effectiveness of all proposed modules.