Federated Learning (FL) allows collaborative training while ensuring data privacy across distributed edge devices, making it a popular solution for privacy-sensitive applications. However, FL faces significant challenges due to statistical heterogeneity, particularly domain heterogeneity, which impedes the global mode’s convergence. In this study, we introduce a new framework to address this challenge by improving the generalization ability of the FL global model under domain heterogeneity, using prototype augmentation. Specifically, we introduce FedAPC (Federated Augmented Prototype Contrastive Learning), a prototype-based FL framework designed to enhance feature diversity and model robustness. FedAPC leverages prototypes derived from the mean features of augmented data to capture richer representations. By aligning local features with global prototypes, we enable the model to learn meaningful semantic features while reducing overfitting to any specific domain. Experimental results on the Office-10 and Digits datasets illustrate that our framework outperforms SOTA baselines, demonstrating superior performance.

In this paper, cyber-attack prevention for the prosumer-based electric vehicle (EV) charging stations (EVCSs) is investigated, which covers two aspects: 1) cyber-attack detection on prosumers’ network traffic (NT) data, and 2) cyber-attack intervention. To establish an effective prevention mechanism, several challenges need to be tackled, for instance, the NT data per prosumer may be non-independent and identically distributed (non-IID), and the boundary between benign and malicious traffic becomes blurred. To this end, we propose an edge-assisted federated prototype knowledge distillation (E-FPKD) approach, where each client is deployed on a dedicated local edge server (DLES) and can report its availability for joining the federated learning (FL) process. Prior to the E-FPKD approach, to enhance accuracy, the Pearson Correlation Coefficient is adopted for feature selection. Regarding the proposed E-FPKD approach, we integrate the knowledge distillation and prototype aggregation technique into FL to deal with the non-IID challenge. To address the boundary issue, instead of directly calculating the distance between benign and malicious traffic, we consider maximizing the overall detection correctness of all prosumers (ODC), which can mitigate the computational cost compared with the former way. After detection, a rule-based method will be triggered at each DLES for cyber-attack intervention. Experimental analysis demonstrates that the proposed E-FPKD can achieve the largest ODC on NSL-KDD, UNSW-NB15, and IoTID20 datasets in both binary and multi-class classification, compared with baselines. For instance, the ODC for IoTID20 obtained via the proposed method is separately 0.3782% and 4.4471% greater than FedProto and FedAU in multi-class classification.

Federated learning-assisted edge intelligence enables privacy protection in modern intelligent services. However, not independent and identically distributed (non-IID) distribution among edge clients can impair the local model performance. The existing single prototype-based strategy represents a class by using the mean of the feature space. However, feature spaces are usually not clustered, and a single prototype may not represent a class well. Motivated by this, this article proposes a multiprototype federated contrastive learning approach (MP-FedCL) which demonstrates the effectiveness of using a multiprototype strategy over a single-prototype under non-IID settings, including both label and feature skewness. Specifically, a multiprototype computation strategy based on k-means is first proposed to capture different embedding representations for each class space, using multiple prototypes$(k$centroids) to represent a class in the embedding space. In each global round, the computed multiple prototypes and their respective model parameters are sent to the edge server for aggregation into a global prototype pool, which is then sent back to all clients to guide their local training. Finally, local training for each client minimizes their own supervised learning tasks and learns from shared prototypes in the global prototype pool through supervised contrastive learning, which encourages them to learn knowledge related to their own class from others and reduces the absorption of unrelated knowledge in each global iteration. Experimental results on MNIST, Digit-5, Office-10, and DomainNet show that our method outperforms multiple baselines, with an average test accuracy improvement of about 4.6% and 10.4% under feature and label non-IID distributions, respectively.

Federated learning is a distributed learning system that addresses the distributed difficulty such as communication overhead and private information in machine learning while maintaining high performance. However, the distributed learners have to dedicate their resources to improving the global model, which is not likely to happen voluntarily. This motivated us to design an incentive mechanism for users (data owners) to actively participate in the FL processes. In this paper, we consider multiple co-existing FL service providers (FLSPs) with the need to train their models and multiple data owners (DOs) that can offer that service. In the system, DO, and FLSP will submit their cost and valuation values to the cloud platform. Based on this information, we formulate an optimization problem that aims to maximize the social welfare under the nonnegative utility constraint and maximum gain of FLSPs. Then, we propose a heuristic algorithm, Binary Whale Optimization Algorithm (B-WOA), that can solve our formulated NP-hard problem in polynomial time. Finally, numerical results are shown to demonstrate the effectiveness of our proposed algorithm. Moreover, we also compare the performance of our proposed algorithm with Hungarian and greedy algorithms.

Nowadays, Federated Learning has emerged as the prominent collaborative learning approach among multiple machine learning techniques. This framework enables communication-efficient and privacy-preserving solution that a group of users interacts with a server to collaboratively train a powerful global model without exchanging users' raw data. However, federated learning might face the significant challenge with high communication cost when exchanging the huge model parameters. Moreover, training such a large model on devices is an obstacle under the battery limitation of mobile devices. To address this hindrance, we propose the federated learning with bi-level distillation, namely FedBD. The key idea of this proposal is to exchange the soft targets instead of transferring the model parameters between server and clients. The exchange knowledge was constructed based on the prediction outcomes for the shared reference dataset. By interchanging the knowledge of the learning models, our algorithm obtains the benefits of reducing both communication and computation costs. The proposed mechanism allows the different model architectures between server and learning agents. The experiments show that our proposed method can achieve comparable or even slightly higher accuracy than FedAvg algorithm on the image classification task while using fewer communication resources and power.