Citation recommendation aims to provide researchers with the most relevant references for their manuscripts, helping them swiftly discover pertinent studies and bolster the reliability of their arguments. However, some individuals manipulate these recommendation systems by injecting false information, such as deliberately inflating the citation count of their own papers, to obtain favorable recommendations and ratings. This form of attack, commonly termed “shilling attack”, is not only highly concealed but also has an unimaginable impact on all scientific research. To address this problem, we theoretically reveal the impact of shilling attacks on citation recommendation and propose three feasible resistance strategies: historical collaborations, significant citations and content constraints. Based on these insights, we introduce RSA-CR, a robust and hybrid citation recommendation algorithm resistant to shilling attacks. The algorithm constructs a two-layer academic graph and uses random and content generation strategies to initialize author and paper embeddings. Confidence-guided inductive aggregations based on collaboration and citation relationships are then performed at the author and paper sides, where author aggregation results directly influences the paper aggregation strength. Finally, recommendations are made by measuring the distances between the fused paper embeddings. The entire learning process resembles a dumbbell, hence termed “dumbbell inductive learning”. Experiments on four academic datasets demonstrate that our method outperforms baselines in both effectiveness and robustness.

Federated learning (FL) has a great potential in large-scale machine learning applications by training a global model over distributed client data. However, FL deployed in real-world applications often incur collaboration bias and unstable convergence with inconsistent local predictions, resulting in poor modelling performance on heterogeneous and long-tailed client data distributions. In this paper, we reconsider heterogeneous FL in a two-stage learning paradigm where representation learning and classifier re-training are separated to incorporate different sampling schemes. This allows us to deal with the dilemma of obtaining more generalizable features and fine tuning a biased classifier building on client model aggregations. Specifically, we propose a novel hybrid knowledge distillation scheme, called FedHyb, to facilitate the two-stage learning. From the view of knowledge transfer, we show that FedHyb enables several desirable properties in the global feature space and optimization with fine-tuning, thus achieving better test accuracy and convergence speed, especially with a higher level of data heterogeneity and an increasing number of distributed clients. FedHyb does not require any information exchange between clients preventing privacy leakage, and is more robust under poisoning attacks comparing with other FL methods designed on heterogeneous data.