Federated learning (FL) is well-suited for multimodal tasks due to its ability to protect privacy and support local training. However, the complexity of real-world sensor environments causes modality heterogeneity across clients. Some modalities may be missing altogether, making it difficult to construct a generalized global model. Existing multimodal federated learning methods often address modality-missing scenarios under simplified assumptions of modality heterogeneity, typically focusing on unimodal clients and modality-complete multimodal clients. Moreover, to mitigate performance degradation caused by missing modalities, some approaches assume the availability of auxiliary information at the server, which may be impractical in real-world scenarios. Therefore, we propose a novel heterogeneous multimodal Federated Learning with Mask-Restoration and Self-Guidance (FL-MRSG). The Mask-Restoration employs a masking strategy to simulate missing data during feature extraction, enabling the network to learn semantic features of missing modality. Furthermore, we introduce an innovative self-guidance mechanism that leverages the restored data as guidance information, enabling the network to distinguish between complete and missing data representations. In addition, we propose a personalized decoupled aggregation strategy to facilitate the collaborative training of a global model across heterogeneous modality clients. We extend the multimodal test set to arbitrary modality combinations to evaluate the robustness of the global model. Extensive experiments on MOSI and SIMS datasets demonstrate the effectiveness of the proposed FL-MRSG for arbitrary missing modalities.

With the large-scale deployment of virtualized data centers, energy consumption and SLA (Service Level Agreement) violation have already become the urgent issue to be solved. And it is essential and important to design energy-aware allocation policy for energy-aware and SLA violation reduction. In this paper, we propose a novel allocation and selection policy for the dynamic virtual machine (VM) consolidation in virtualized data centers to reduce energy consumption and SLA violation. Firstly, we use the mean and standard deviation of CPU utilization for VM to determine the hosts overloaded or not, secondly we use the positive maximum correlation coefficient to select VMs from those overloading hosts for migration. Although the proposed allocation and selection policies performs a little worse than the previous ones in energy consumption, experiments show that it performs greatly better than the previous ones on the whole.