Machine unlearning is a critical area of research aimed at safeguarding data privacy by enabling the removal of sensitive information from machine learning models. One unique challenge in this field is catastrophic unlearning, where erasing specific data from a well-trained model unintentionally removes essential knowledge, causing the model to deviate significantly from a retrained one. To address this, we introduce a novel approach that regularizes the unlearning process by utilizing synthesized mixup samples, which simulate the data susceptible to catastrophic effects. At the core of our approach is a generator-unlearner framework, MixUnlearn, where a generator adversarially produces challenging mixup examples, and the unlearner effectively forgets target information based on these synthesized data. Specifically, we first introduce a novel contrastive objective to train the generator in an adversarial direction: generating examples that prompt the unlearner to reveal information that should be forgotten, while losing essential knowledge. Then the unlearner, guided by two other contrastive loss terms, processes the synthesized and real data jointly to ensure accurate unlearning without losing critical knowledge, overcoming catastrophic effects. Extensive evaluations across benchmark datasets demonstrate that our method significantly outperforms state-of-the-art approaches, offering a robust solution to machine unlearning. This work not only deepens understanding of unlearning mechanisms but also lays the foundation for effective machine unlearning with mixup augmentation.

Mining relevant stocks given a trending topic/concept in capital markets is an application with significant economic and societal impacts. Previous concept stock recommendation system mines concept stocks only from public social media like financial news. On stock forums, investors discuss emerging concepts and stocks by using forum comments, which are unneglectable resources to capture trending concept stocks accurately and timely. However, the comment data from a single forum is insufficient to build a high-quality recommendation system. The forums are data silos protected by privacy regulations, and their comments are still underutilized. In this paper, we propose a federated concept stock recommendation baseline and an optimized method that both leverage the private forum comments and public social media without compromising privacy regulations. Our baseline,i.e., Federated Meta Embedding (FedME), is built upon the federated learning framework and learns a concept-stock embedding jointly from private and public data. Our optimized method, Federated Graph Meta Embedding (FedGME), improves FedME by using a graph to combine two sources of embeddings and additional human experts' concept-stock knowledge. Empirically, the experiments on two concept stock datasets show that FedME and FedGME substantially improve the performance of recommendation. Our methods provide practical guidance on privacy-preserving FinTech applications.