Multi-view spectral clustering (MVSC) has become a popular approach to harvest knowledge about group information from multiple views of data, owned by different parties. A high quality MVSC approach usually requires collecting massive amount of data from each view party, in order to perform MVSC algorithm in a centralized manner. However, such centralized MVSC approach raises serious privacy concerns, not only in terms of the sensitivity property of many real-world data such as medical or financial records, but also in terms of the regulations from authorities to preclude centralized operations. Hence it is crucial to design new paradigm for training spectral clustering model on multi-view data in industrial scenarios. In this article, we propose a distributed and secure framework named Federated Multi-view Spectral Clustering (FMSC), in which a group of view parties collaboratively perform a MVSC model, but couldn’t learn the data of other participants. FMSC is inspired by the concept of federated learning, and utilize Homomorphic Encryption (HE) and Differential Privacy (DP) to achieve secure and private clustering. We conduct a series of extensive experiments to verify the effectiveness of FMSC on both synthetic and real-world datasets. Evaluations show that FMSC achieves respectable clustering results over conventional centralized approaches.

With the growing application of artificial intelligence (AI) in the legal domain, large language model (LLM)-based legal agents have achieved remarkable progress. This survey provides a comprehensive review of the applications and developments of LLM-driven agents in law. Firstly, we outline the core legal tasks, including legal information retrieval, question answering, judgment prediction, and legal text generation, along with the corresponding evaluation benchmarks. Then, we analyze the technical challenges faced by both single-agent and multi-agent systems in legal scenarios and summarize the prevailing research methods. Finally, we discuss future directions for legal agents, including enhancing single-agent trustworthiness through explainability, boosting multi-agent efficiency with collaborative AI techniques, enabling cross-jurisdictional interoperability via legal knowledge graphs, and establishing ethical governance with quantifiable metrics. By synthesizing existing research, this survey aims to offer theoretical insights and practical guidance for the sustainable advancement of legal agents.

Cloud service providers typically co-locate various workloads within the same production cluster to improve resource utilization and reduce operational costs. These workloads primarily consist of batch analysis jobs composed of multiple parallel short-running tasks and long-running applications (LRAs) that continuously reside in the system. The adoption of microservice architecture has led to the emergence of distributed long-running applications (DLRAs), which enhance deployment flexibility but pose challenges in detecting and investigating QoS violations due to workload variability and performance propagation across microservices. State-of-the-art resource managers are only responsible for resource allocation among applications/jobs and do not prioritize runtime QoS aspects, such as applicationlevel latency. To address this, we introduce Prank, a QoSdriven resource management framework for co-located workloads. Prank incorporates a non-intrusive performance anomaly detection mechanism for DLRAs and proposes a root cause localization algorithm based on PageRank-weighted analysis of performance anomalies. Moreover, it dynamically balances resource allocation between DLRAs and co-located batch jobs on nodes hosting critical microservices, optimizing for both DLRA performance and overall cluster efficiency. Experimental results demonstrate that Prank outperforms state-of-the-art baselines, reducing DLRA tail latency by over 38% while increasing batch job completion time by no more than 21% on average