No abstract

Client contribution evaluation is crucial in federated learning(FL) to effectively select influential clients. Contrary to data valuation in centralized settings, client contribution evaluation in FL faces a lack of data accessibility and consequently challenges stable quantification of the impact of data heterogeneity. To address this instability of client contribution evaluation, we introduce an empirical method, Federated Client Contribution Evaluation through Accuracy Approximation(FedCCEA), which exploits data size as a tool for client contribution evaluation. After several FL simulations, FedCCEA approximates the test accuracy using the sampled data size and extracts the client contribution from the trained accuracy approximator. In addition, FedCCEA grants data size diversification, which reduces the massive variation in accuracy resulting from game-theoretic strategies. Several experiments have shown that FedCCEA strengthens the robustness to diverse heterogeneous data environments and the practicality of partial participation.

Figure 1: An outline of an Img2Tab example in a gender classification. A) Img2Tab predicts its label of the image by tabular features encoded from the image, not by pixels. B) Img2Tab explains its prediction by feature importance and visualization at the concept level. We visualize concepts in the red boxes by latent modifications. Important features (#k1, #k2, and #k3) extracted by the tabular classifier represent class-relevant concepts ("Hair length," "Mustache," and "Lipstick," respectively).

Developing an accurate tourism forecasting model is essential for making desirable policy decisions for tourism management. Early studies on tourism management focus on discovering external factors related to tourism demand. Recent studies utilize deep learning in demand forecasting along with these external factors. They mainly use recursive neural network models such as LSTM and RNN for their frameworks. However, these models are not suitable for use in forecasting tourism demand. This is because tourism demand is strongly affected by changes in various external factors, and recursive neural network models have limitations in handling these multivariate inputs. We propose a multi-head attention CNN model (MHAC) for addressing these limitations. The MHAC uses 1D-convolutional neural network to analyze temporal patterns and the attention mechanism to reflect correlations between input variables. This model makes it possible to extract spatiotemporal characteristics from time-series data of various variables. We apply our forecasting framework to predict inbound tourist changes in South Korea by considering external factors such as politics, disease, season, and attraction of Korean culture. The performance results of extensive experiments show that our method outperforms other deep-learning-based prediction frameworks in South Korea tourism forecasting.