In modern industrial systems, components have complex interactions with each other, which makes it become a challenging task to identify the operational conditions of industrial systems. Considering that an industrial system, the embedded components and their interactions can be expressed as nodes and edges in a graph, respectively. Therefore, graph representation algorithms are powerful tools for fault diagnosis of industrial systems. As one of the most commonly used graph representation algorithms, graph neural networks (GNN) mainly follow the law of “learning to attend.” GNN extract training data features learn the statistical correlations between features and labels, resulting in the attended graph favoring for accessing noncausal features as a shortcut for prediction. This shortcut feature is unstable and depends on the data distribution characteristics in the training dataset, which reduces the generalization ability of the classifier. By performing the causal analysis of GNN modeling for graph representation, the results show that shortcut features act as confounding factors between causal features and predictions, causing classifiers to learn wrong correlations. Therefore, to discover patterns of causality and weaken the confounding effects of shortcut features, a causal-trivial attention graph neural network strategy is proposed. First, node and edge representations are given by estimating soft masks. Second, through disentanglement, both causal features and shortcut features are obtained from the graph. Third, the backdoor adjustment of the causal theory is parameterized to combine each causal feature with a variety of shortcut features. Finally, comparative experiments on the three-phase flow facility dataset illustrate the effectiveness of the proposed method.

Multi-Party Computation (MPC) provides an effective cryptographic solution for distributed computing systems so that local models with sensitive information are encrypted before sending to the centralized servers for aggregation. Though direct local knowledge leakages are eliminated in MPC-based algorithms, we observe the server can still obtain the local information indirectly in many scenarios, or even reveal the groundtruth images through methods like Deep Leakage from Gradients (DLG). To eliminate such possibilities and provide stronger protections, we propose an augmented MPC approach by encrypting local models with two rounds of decomposition before transmitting to the server. The proposed solution allows us to remove the constraint that servers must be honest in the general federated learning settings since the true global model is hidden from the servers. Specifically, the augmented MPC algorithm encodes local models into multiple secret shares in the first round, then each share is furthermore split into a public share and a private share. Consequences of such a two-round decomposition are that the augmented algorithm fully inherits the advantages of standard MPC by providing lossless encryption and decryption while simultaneously rendering the global model invisible to the central server. Both theoretical analysis and experimental verification demonstrate that such an augmented solution can provide stronger protections for the security and privacy of the training data, with minimal extra communication and computation costs incurred.

The integration of Foundation Models (FMs) with Federated Learning (FL) presents a transformative paradigm in Artificial Intelligence (AI). This integration offers enhanced capabilities, while addressing concerns of privacy, data decentralization and computational efficiency. This paper provides a comprehensive survey of the emerging field of Federated Foundation Models (FedFM), elucidating their synergistic relationship and exploring novel methodologies, challenges, and future directions that the FL research field needs to focus on in order to thrive in the age of FMs. A systematic multi-tiered taxonomy is proposed, categorizing existing FedFM approaches for model training, aggregation, trustworthiness, and incentivization. Key challenges, including how to enable FL to deal with high complexity of computational demands, privacy considerations, contribution evaluation, and communication efficiency, are thoroughly discussed. Moreover, this paper explores the intricate challenges of communication, scalability and security inherent in training/fine-tuning FMs via FL. It highlights the potential of quantum computing to revolutionize the processes of training, inference, optimization and security. This survey also introduces the implementation requirement of FedFM and some practical FedFM applications. It highlights lessons learned with a clear understanding of our findings for FedFM. Finally, this survey not only provides insights into the current state and challenges of FedFM, but also offers a blueprint for future research directions, emphasizing the need for developing trustworthy solutions. It serves as a foundational guide for researchers and practitioners interested in contributing to this interdisciplinary and rapidly advancing field.

The lack of complete labels and data heterogeneity are obstacles to the application of artificial intelligence-based methods in industrial scenarios, such as machinery fault diagnosis. To address these challenges, this article proposes a federated learning (FL) framework for the industrial Internet of Things based on bidirectional knowledge distillation (KD) and hard sample selection. In the framework, the cloud server provides a pretrained deep learning (DL) model based on the cross-domain public dataset to facilitate the cold start in real-world applications. Then during the training process, each participating factory trains its heterogeneous local DL model according to local data volume and computing resources. Bidirectional KD with feature maps and hard sample selection is then carried out on a shared dataset between the server and factories to share knowledge efficiently. Moreover, all the DL models used in the application of the proposed framework are designed based on expertise and attention mechanism to diagnose multiple types of machinery and faults. Case studies using the vibration data collected from multiple factories show that the proposed framework improves the fault diagnosis accuracy compared to other FL methods while significantly reducing communication overhead.

Artificial Intelligence (AI) has been widely applied for Safety Inspection in a number of industrial domains. However, individual company usually could not provide sufficient data to support well-trained AI models. Federated Learning, as a new AI paradigm, enables a number of participants to contribute training data to co-create high-performance models without compromising data privacy. However, an effective incentive mechanism is essential to encourage participants to contribute high-quality data, and the fundamental of the incentive mechanism is to evaluate participants' contribution fairly. Shapley Value (SV) is a well-known approach to evaluate individual's marginal contribution in a coalition, but the canonical SV calculation and its available variants are very costly. In this paper, we proposed an FL framework to enable a number of natural gas companies from different cities to jointly train an object detection computer vision deep learning model for the purpose of identifying potential hazards, without sharing their confidential inspection photos directly. We improve state-of-the-art SV algorithm by proposing Weighted Truncation (WT) for unnecessary computations, and go further with Dynamic Programming (WTDP), achieving better trade-off between efficiency and accuracy. Based on our proposed WTDP-Shapley participant contribution evaluation approach, an effective end-to-end incentive mechanism is designed by leveraging knowledge of both data scientists and domain experts. According to our experiments, it could encourage participants to contribute scarce photos with potential hazards, and thus co-create a high-performance AI model to identify various hazards accurately for residential natural gas installation safety inspection.