Web platforms are reshaping resource allocation in distributed energy networks globally, from off-grid communities to lunar bases. Algorithmic decision-makers face the fundamental challenge of fairly distributing scarce resources among heterogeneous stakeholders. Traditional approaches assume complete rationality with perfect information and unlimited computation, yet distributed networks only permit local observation, requiring fairness to emerge from individual strategic interactions. Centralized optimization fails due to exponential complexity, rule-based methods cannot adapt to disruptions, and existing platforms translate economic inequality into energy access inequality. Recognizing the unattainability of complete rationality necessitates bounded rationality: pursuing provably convergent satisficing solutions under incomplete information and limited computation, translating natural language ethics into computable constraints, and designing incentives so self-interested behavior satisfies fairness at equilibrium. We propose a unified semantic-game-distributed framework. Large language models map ambiguous ethical principles into game-theoretic parameters through semantic parameterization, with contrastive learning ensuring semantic consistency and temporal stability. A two-layer Stackelberg game implements incentive design: the platform signals through differentiated pricing while nodes optimize locally, enabling fairness to emerge from equilibrium. Distributed asynchronous iteration achieves global convergence through local communication, with cognitive models adaptively adjusting step sizes and differential perturbation preserving privacy. Theoretical analysis establishes equilibrium existence and convergence guarantees, while extreme scenarios validate robustness under information scarcity and high uncertainty.

Despite Federated Learning (FL) employing gradient aggregation at the server for distributed training to prevent the privacy leakage of raw data, private information can still be divulged through the analysis of uploaded gradients from clients. Substantial efforts have been made to integrate local differential privacy (LDP) into the system to achieve a strict privacy guarantee. However, existing methods fail to take practical issues into account by merely perturbing each sample with the same mechanism while each client may have their own privacy preferences on privacy-sensitive information (PSI), which is not uniformly distributed across the raw data. In such a case, excessive privacy protection from private-insensitive information can additionally introduce unnecessary noise, which may degrade the model performance. In this work, we study the PSI within data and develop FedRE, that can simultaneously achieve robustness and effectiveness benefits with LDP protection. More specifically, we first define PSI with regard to the privacy preferences of each client. Then, we optimize the LDP by allocating less privacy budget to gradients with higher PSI in a layer-wise manner, thus providing a stricter privacy guarantee for PSI. Furthermore, to mitigate the performance degradation caused by LDP, we design a parameter aggregation mechanism based on the distribution of the perturbed information. We conducted experiments with text tamper detection on T-SROIE and DocTamper datasets, and FedRE achieves competitive performance compared to state-of-the-art methods.

The current Federated Recommendation System (FedRS) focuses on personalized recommendation services and assumes clients are personalized IoT devices (e.g., Mobile phones). In this paper, we deeply dive into new but practical FedRS applications within the joint venture ecosystem. Subsidiaries engage as participants with their users and items. However, in such a situation, merely exchanging item embedding is insufficient, as user bases always exhibit both overlaps and exclusive segments, demonstrating the complexity of user information. Meanwhile, directly uploading user information is a violation of privacy and unacceptable. To tackle the above challenges, we propose an efficient and privacy-enhanced federated recommendation for the joint venture ecosystem (FR-JVE) that each client transfers more common knowledge from other clients with a distilled user's \textit{rating preference} from the local dataset. More specifically, we first transform the local data into a new format and apply model inversion techniques to distill the rating preference with frozen user gradients before the federated training. Then, a bridge function is employed on each client side to align the local rating preference and aggregated global preference in a privacy-friendly manner. Finally, each client matches similar users to make a better prediction for overlapped users. From a theoretical perspective, we analyze how effectively FR-JVE can guarantee user privacy. Empirically, we show that FR-JVE achieves superior performance compared to state-of-the-art methods.

Multimodal federated learning (MMFL) has gained increasing popularity due to its ability to leverage the correlation between various modalities, meanwhile preserving data privacy for different clients. However, recent studies show that correlation between modalities increase the vulnerability of federated learning against Gradient Inversion Attack (GIA). The complicated situation of MMFL privacy preserving can be summarized as follows: 1) different modality transmits different amounts of information, thus requires various protection strength; 2) correlation between modalities should be taken into account. This paper introduces an information theory perspective to analyze the leaked privacy in process of MMFL, and tries to propose a more reasonable protection method \textbf{Sec-MMFL} based on assessing different information leakage possibilities of each modality by conditional mutual information and adjust the corresponding protection strength. Moreover, we use mutual information to reduce the cross-modality information leakage in MMFL. Experiments have proven that our method can bring more balanced and comprehensive protection at an acceptable cost.

Federated Recommendation System (FRS) usually offers recommendation services for users while keeping their data locally to ensure privacy. Currently, most FRS literature assumes that fixed users participate in federated training with personal IoT devices (e.g., mobile phones and PC). However, users may join incrementally, and retraining the entire FRS for each new participating user is unfeasible due to the high training costs and the limited global knowledge contribution from a small number of new users. To guarantee the quality service for these new users, we take a dive into the federated recommendation for cold-start users, a novel scenario where the new participating users can directly obtain a promising recommendation without comprehensive training with all participating users by leveraging both transferred knowledge from the converged warm clients and the knowledge learned from the local data.

Recently, there are many approaches proposed for mining roles using automated technologies. However, it lacks a tool set that can be used to aid the application of role mining approaches and update role states. In this demonstration, we introduce a tool set, RMiner, which is based on the core of WEKA, an open source data mining tool. RMiner implements most of the classic and latest role mining algorithms and provides interactive tools for administrator to update role states. The running examples of RMiner are presented to demonstrate the effectiveness of the tool set.

Cloud-oriented service mashup can aggregate many services to provide personalized services for end-users on demand. However, how to securely aggregate mashup services becomes a bottleneck of hampering the development of cloud computing. In this paper, we present a secure cloud service mashup framework called SMEF to address this problem. In SMEF, we employ security entropy to measure the unascertained security degree of service mashup. The nonfunctional criteria of SMEF are aggregated as a single criterion by defining a utility function. Then the relatively optimal mashup services are selected to meet the user requirements. Finally, we have implemented a simulation of SMEF and conducted extensive experiments using simulations of different sizes of services and security factors. Experimental results show the feasibility and efficiency of the SMEF service mashup framework.