By providing recommendation services while preserving user data privacy, federated sequential recommendation (FSR) achieves great attention recently. However, FSR suffers from the severe challenge of data sparsity, where item interaction sequences of each user are typically short. The common practice to address the problem is to employ various data augmentation operators such as crop or replace to generate more sequences. While generally adopted, we identify that these operators will bring additional noise, leading to unstable performance. To this end, this paper proposes FR-AKP, which performs the augmented knowledge purification on the basis of data augmentation at both the intra-client and inter-client levels. At the intra-client level, each client first applies augmentation operators to local sparse data, then transforms them into the frequency domain via Fast Fourier Transform, and finally employs a personalized frequency selection mechanism to denoise the augmented data. At the inter-client level, to prevent the propagation of noisy knowledge among clients during global aggregation, the server further adopts a fusion-network-based aggregation mechanism that generates personalized aggregation weights based on the distributional differences of the frequency-domain features of clients. Experimental results on four real-world datasets demonstrate that the proposed method significantly outperforms state-of-the-art methods by up to 34.62% in Recall and 29.92% in NDCG.
Yijing Shan, Haozhao Wang, Yi-Chen Li et al.· Proceedings of the 32nd ACM...· 0 citations
Multi-agent systems (MAS) are enabling increasingly complex, collaborative applications in autonomous driving, smart logistics, robotic coordination, and distributed sensing. Their effectiveness depends on collective intelligence emerging from multiple distributed agents, each operating with partial information and often sensitive local data. To realize such collaboration while preserving data privacy and autonomy, federated learning (FL) has emerged as a de facto decentralized framework that allows agents to learn shared models without centralizing raw data. Despite the rapid progress in both FL and MAS, significant challenges remain in integrating these paradigms—such as coordinating heterogeneous agents, handling non-IID data, ensuring communication efficiency, and maintaining system robustness and fairness in open environments. This workshop seeks to bring together researchers and practitioners from academia and industry to explore the convergence of federated learning and multi-agent systems. We aim to foster discussions on foundational advances, real-world deployments, and emerging interdisciplinary opportunities, with a focus on scalability, trustworthiness, adaptive coordination, and the broader societal impact of federated multi-agent intelligence.
Haozhao Wang, Zhuangdi Zhu, Zheng Xu et al.· Proceedings of the 32nd ACM...· 0 citations
A novel method named IED is proposed, which leverages the information gain to construct the vector of which each dimension represents the information entropy of each word, and then adopts a classifier to conduct the detection.
