In federated recommendation systems, model poisoning attacks aim to manipulate the gradient information of multiple target items sent back from local clients to the central server, with the goal of abnormally increasing their exposure across the system. Existing multi-target attack approaches directly manipulate multiple target items and apply a uniform attack strategy to all target items, which may lead to suboptimal promotion effectiveness. To address this issue, we introduce ProitMTA, a novel multi-target model poisoning attack framework that introduces proxy items and provides tailored attack strategies for target items. ProitMTA employs a three-stage process that balances the promotion of multiple target items while preserving recommendation quality. First,proxy item generationuses a Gaussian Mixture Model to create proxy items that represent diverse attack strategies. Second,proxy attack constructiondesigns customized gradient manipulation strategies for each proxy item. Finally,proxy-based target item attacktransfers these strategies to actual target items, enhancing their promotion while minimizing the negative impact on system performance. Through comprehensive experiments on multiple base federated recommendation frameworks and diverse real-world datasets, we demonstrate that ProitMTA outperforms existing attack methods, achieving higher success rates in target item promotion with minimal system-wide performance degradation. Our research highlights the vulnerability of federated recommendation systems when facing multi-target poisoning attacks and underscores the importance of researching effective defense mechanisms We have released our code athttps://github.com/zdy769243418/ProitMTA.

Large Language Models (LLMs) have demonstrated powerful performance in sequential recommendation due to their robust language modeling and comprehension capabilities. In such paradigms, the item texts of interaction sequences are formulated as sentences and LLMs are utilized to learn language representations or directly generate target item texts by incorporating instructions. Despite their promise, these methods solely focus on modeling the mapping from sequential texts to target items, neglecting the relationship between the items in an interaction sequence. This results in a failure to learn the transition patterns between items, which reflect the dynamic change in user preferences and are crucial for predicting the next item. To tackle this issue, we propose a novel framework for mapping the sequential item texts to the sequential item IDs, named ST2SI. Specifically, we first introduce multi-query input and item linear projection (ILP) to model the conditional probability distribution of items. Then, we further propose ID alignment to address misalignment between item texts and item IDs by instruction tuning. Finally, we propose efficient ILP tuning to adapt flexibly to different scenarios, requiring only training a linear layer to achieve competitive performance. Extensive experiments on six real-world datasets show our approach outperforms the best baselines by 7.33% in NDCG@10, 4.65% in Recall@10, and 8.42% in MRR.

Cross-domain sequential recommendation (CSR) has garnered significant attention. Current federated frameworks for CSR leverage information across multiple domains but often rely on user alignment, which increases communication costs and privacy risks. In this work, we propose FedCSR, a novel federated cross-domain sequential recommendation framework that eliminates the need for user alignment between platforms. FedCSR fully utilizes cross-domain knowledge to address the key challenges related to data heterogeneity both inter- and intra-platform. To tackle the heterogeneity of data patterns between platforms, we introduce Model Contrastive Learning (MCL) to reduce the gap between local and global models. Additionally, we design Sequence Contrastive Learning (SCL) to address the heterogeneity of user preferences across different domains within a platform by employing tailored sequence augmentation techniques. Extensive experiments conducted on multiple real-world datasets demonstrate that FedCSR achieves superior performance compared to existing baseline methods.

Federated Graph Learning (FGL) enables multiple clients to collaboratively learn node representations from private subgraph data, such as user transactions or social networks. Local models are trained on clients and then aggregated by a central server, supporting large-scale graph learning without sharing raw data. However, most existing FGL methods assume that the number of nodes in the graph remains constant, while real-world scenarios often evolve, with new nodes and edges continually added and older ones removed due to limited device memory. We define this setting as Federated Continual Graph Learning (FCGL). In FCGL, global model aggregation may cause interference occur inter-task and inter-client, therefore, FCGL suffers from the global catastrophic forgetting: as the global model adapts to newly added nodes, it loses knowledge acquired from earlier graph data of clients. To address this, we propose GRE-FL, a generative replay framework, which can mitigate global catastrophic forgetting by generating a global summary graph at the server to preserve critical information from historical nodes. It also improves performance by equipping local models with a gating graph attention network for better feature extraction. Experiments show that GRE-FL achieves strong performance across multiple datasets.

Cross-domain Sequential Recommendation (CSR) which leverages user sequence data from multiple domains has received extensive attention in recent years. However, the existing CSR methods require sharing origin user data across domains, which violates the General Data Protection Regulation (GDPR). Thus, it is necessary to combine federated learning (FL) and CSR to fully utilize knowledge from different domains while preserving data privacy. Nonetheless, the sequence feature heterogeneity across different domains significantly impacts the overall performance of FL. In this paper, we propose FedDCSR, a novel federated cross-domain sequential recommendation framework via disentangled representation learning. Specifically, to address the sequence feature heterogeneity across domains, we introduce an approach called inter-intra domain sequence representation disentanglement (SRD) to disentangle the user sequence features into domain-shared and domain-exclusive features. In addition, we design an intra domain contrastive infomax (CIM) strategy to learn richer domain-exclusive features of users by performing data augmentation on user sequences. Extensive experiments on three real-world scenarios demonstrate that FedDCSR achieves significant improvements over existing baselines1.