With the popularity of federated learning, federated domain generalization (FedDG) has attracted more and more attention. Existing works of federated learning indicate that the generalization performance of the global model can be improved when the global model is obtained by aggregating local models according to suitable weights. However, existing methods to calculate weights do not fully utilize the data influences on the global model update, which gives us an opportunity to improve the generalization performance of the global model further. In this paper, we propose the method DI (data influences), which utilizes data influences on the global model update to calculate dynamical weights of local model in each round of training. Specifically, the first component data influence calculator (DIC) of DI calculates local weights of local model from the influences of data on the global model update and we introduce the influence function to complete the calculation process. The second component data influence adjuster (DIA) of DI calculates global weights (which are used in the aggregation process of the global model) from local weights. Extensive experiments indicate that our method improves the generalization performance of models significantly. In particular, our method improves model accuracy on benchmark datasets PACS, OfficeHome, and Office-31 by 1.79%, 1.61%, and 2.39% on average, respectively. Source code is publicly available at github-https://github.com/zikunZHOUHH/Fed-DI.

Betweenness centrality is one of the key centrality measures in many applications including community detections in biological networks, vulnerability detections in communication networks, misinformation filtering in social networks, etc. The top-K group betweenness centrality problem is to find a group of K nodes from a network so that the total fraction of shortest paths that pass through the K nodes is maximized. Existing studies proposed randomized sampling algorithms for the problem. We notice that the existing studies ensured that, the maximum deviation of the estimated centrality of every group from its expectation is no greater than a small given threshold for all potential groups with no more than K nodes, thereby generating too many samples, as the number of such groups is prohibitively large. In contrast, in this paper we first devise a novel algorithm that enables to estimate the centrality of a tentative group adaptively, and the algorithm immediately stops once the centrality is large enough; otherwise, the algorithm uses more samples to find a better group. We then theoretically show that, even the proposed algorithm uses much less samples, it still can find a performance-guaranteed group with high probability. Experimental results with real-world networks demonstrate that the number of samples used by the proposed algorithm is up to 36 times smaller than the state-of-the-art, while the centrality of the group found by the algorithm is no more than 4.5% smaller than the latter.

Betweenness centrality is one of the key centrality measures in many applications including community detections in biological networks, vulnerability detections in communication networks, misinformation filtering in social networks, etc. The top-$K$group betweenness centrality problem is to find a group of$K$nodes from a network so that the total fraction of shortest paths that pass through the$K$nodes is maximized. Existing studies proposed randomized sampling algorithms for the problem. We notice that the existing studies ensured that, the maximum deviation of the estimated centrality of every group from its expectation is no greater than a small given threshold for all potential groups with no more than$K$nodes, thereby generating too many samples, as the number of such groups is prohibitively large. In contrast, in this paper we first devise a novel algorithm that enables to estimate the centrality of a tentative group adaptively, and the algorithm immediately stops once the centrality is large enough; otherwise, the algorithm uses more samples to find a better group. We then theoretically show that, even the algorithm uses much less samples, it still can find a performance-guaranteed group with a large success probability. Experimental results with real-world networks demonstrate that the number of samples used by the proposed algorithm is from 2 to 18 times smaller than the state-of-the-art, while the centrality of the group found by the algorithm is no more than 4% smaller than the latter.

Accurately forecasting long-term future wind power is critical to achieve safe power grid integration. This problem is quite challenging due to wind power's high volatility and randomness. In this paper, we propose a novel time series forecasting method, namely Deep Conditional Generative Spatio-Temporal model (DCGST), and its high accuracy is achieved by tackling two critical issues simultaneously: a proper handling of the non-stationarity of multiple wind power time series, and a fine-grained modeling of their complicated yet dynamic spatio-temporal dependencies. Specifically, we first formally define theSpatio-Temporal Concept Drift(STCD) problem of wind power, and then we propose a novel deep conditional generative model to learn probabilistic distributions of future wind power values under STCD. Three different tailored neural networks are designed for distributions parameterization, including a graph-based prior network, an attention-based recognition network, and a stochastic seq2seq-based generation network. They are able to encode the dynamic spatio-temporal dependencies of multiple wind power time series and infer one-to-many mappings for future wind power generation. Compared to existing methods, DCGST can learn better spatio-temporal representations of wind power data and learn better uncertainties of data distribution to generate future values. Comprehensive experiments on real-world datasets including the largest public turbine-level wind power dataset verify the effectiveness, efficiency, generality and scalability of our method.

Conversational recommender systems model user dynamic preferences and recommend items based on multi-turn interactions. Though the conversational recommender system has achieved good performance, it has two limitations. On the one hand, researchers usually random select an anchor item from user's historical interactions to simulate the interaction with the real user, but some items in the historical interactions do not fit the user realistic preferences (item noise). On the other hand, it pays too much attention to user dynamic preferences, but nurses some static preferences that are difficult to change over a short period. In fact, when there is no explicit attribute preference in user's conversation, the user static preferences can also be used to make recommendations. To address the aforementioned issues, a novel method that combines graph path reasoning with multi-turn conversation is proposed, called Graph Path reasoning for conversational Recommendation (GPR). In GPR, a soft-clustering is designed to classify items and then set operations are utilized to filter the noise in the user's historical interactions. To capture user dynamic preferences and take account of the user inherent static preferences, GPR asks questions about attributes in the attribute-level reasoning and asks whether the items fit user static preferences in the item-level reasoning on a heterogeneous graph. In the multi-turn of two-level graph path reasoning, a reinforcement learning is used to obtain the optimal path and accurately recommend items to users. Extensive experiments conducted on two benchmark datasets verify that GPR can significantly improve recommendation performance and reduce the turn of path reasoning.