Clustering non-independent and identically distributed (non-IID) data under local differential privacy (LDP) in federated settings presents a critical challenge: preserving privacy while maintaining accuracy without iterative communication. Existing one-shot methods rely on unstable pairwise centroid distances or neighborhood rankings, degrading severely under strong LDP noise and data heterogeneity. We present Gravitational Federated Clustering (GFC), a novel approach to privacy-preserving federated clustering that overcomes the limitations of distance-based methods under varying LDP. Addressing the critical challenge of clustering non-IID data with diverse privacy guarantees, GFC transforms privatized client centroids into a global gravitational potential field where true cluster centers emerge as topologically persistent singularities. Our framework introduces two key innovations: (1) a client-side compactness-aware perturbation mechanism that encodes local cluster geometry as "mass" values, and (2) a server-side topological aggregation phase that extracts stable centroids through persistent homology analysis of the potential field's superlevel sets. Theoretically, we establish a closed-form bound between the privacy budget ε and centroid estimation error, proving the potential field's Lipschitz smoothing properties exponentially suppress noise in high-density regions. Empirically, GFC outperforms state-of-the-art methods on ten benchmarks, especially under strong LDP constraints (ε < 1), while maintaining comparable performance at lower privacy budgets. By reformulating federated clustering as a topological persistence problem in a synthetic physics-inspired space, GFC achieves unprecedented privacy-accuracy trade-offs without iterative communication, providing a new perspective for privacy-preserving distributed learning.

Large language models (LLMs) have been widely used for automated negotiation, but their high computational cost and privacy risks limit deployment in privacy-sensitive, on-device settings such as mobile assistants or rescue robots. Small language models (SLMs) offer a viable alternative, yet struggle with the complex emotional dynamics of high-stakes negotiation. We introduce EmoMAS, a Bayesian multi-agent framework that transforms emotional decision-making from reactive to strategic. EmoMAS leverages a Bayesian orchestrator to coordinate three specialized agents: game-theoretic, reinforcement learning, and psychological coherence models. The system fuses their real-time insights to optimize emotional state transitions while continuously updating agent reliability based on negotiation feedback. This mixture-of-agents architecture enables online strategy learning without pre-training. We further introduce four high-stakes, edge-deployable negotiation benchmarks across debt, healthcare, emergency response, and educational domains. Through extensive agent-to-agent simulations across all benchmarks, both SLMs and LLMs equipped with EmoMAS consistently surpass all baseline models in negotiation performance while balancing ethical behavior. These results show that strategic emotional intelligence is a key driver of negotiation success. By treating emotional expression as a strategic variable within a Bayesian multi-agent optimization framework, EmoMAS establishes a new paradigm for effective, private, and adaptive negotiation AI suitable for high-stakes edge deployment. The code is available at https://github.com/Yunbo-max/EmoMAS.

Link prediction plays a vital role in uncovering hidden relationships within complex networks, enabling applications such as identifying potential customers and products. However, this task faces critical challenges, including growing concerns over data privacy and the substantial computational and storage costs associated with large-scale networks. Condensed graphs, which are significantly smaller yet retain essential structural information, have emerged as a promising solution for preserving data utility while enhancing privacy. Despite this potential, existing methods like HyDRO rely on random node selection strategies designed primarily for node classification, overlooking connectivity patterns crucial for effective link prediction. Moreover, these methods lack rigorous evaluations of privacy risks associated with nodes and links in the condensed graphs—an essential consideration for sensitive real-world networks, where privacy concerns far exceed those of public datasets such as citation graphs. To address these limitations, we introduce HyDRO + , a novel graph condensation method guided by algebraic Jaccard similarity. By leveraging local connectivity patterns, HyDRO + generates structurally-aware condensed graphs that preserve link information more effectively. We further introduce a comprehensive evaluation framework that rigorously assesses both node- and link-level privacy leakage. Extensive experiments on four real-world networks demonstrate that HyDRO + consistently outperforms state-of-the-art methods—and even the original networks—in balancing link prediction accuracy and privacy preservation. Notably, it achieves nearly 20 × faster training and reduces storage requirements by a factor of 452 on the Computers dataset. This work represents the first attempt to use condensed graphs for privacy-preserving link prediction in real-world complex networks, offering a practical and scalable solution for secure information sharing in large-scale networks.

In the situation that the modular multilevel converter (MMC) needs to maintain a good current control performance within a wide scope of operations such as the intermittent new energy grid connection and electric locomotive, etc, this paper starts from the output current slope generated after the output voltage and system voltage of MMC converter are jointly acted on the equivalent coupling reactance to analyze the mathematic relation among the current error, output voltage and other correlatives and propose the MMC current hysteresis control method based on output current slope. This method is characterized by simple algorithm, definite physical significance, high output accuracy, small output current ripple, fast dynamic response and strong robustness, etc. Finally, the effectiveness and feasibility of this method is verified by the physical controller - RTDC close-loop real-time simulation experiment.