Federated learning enables a server to train a global model by leveraging data distributed across multiple clients. However, its inherently distributed and iterative nature introduces significant communication overhead as well as potential privacy concerns. To mitigate these issues, one-shot federated learning restricts communication between the server and clients to a single round. Nevertheless, this constraint often results in reduced accuracy. In the field of medical diagnosis, data security is particularly critical and has become a major research focus. In this paper, we propose a one-shot federated learning framework and conduct experiments on three popular datasets and a melanoma image dataset collected by ourselves. To further enhance performance, we introduce One-shot Federated Learning framework for Melanoma Diagnosis, OFL-MD, which incorporates a pre-trained diffusion model CLIP to assist clients in generating synthetic datasets and employs differential privacy to further enhance the privacy. The synthetic datasets are then transmitted to the global model for lightweight fine-tuning, thereby improving accuracy. We evaluate our approach on both widely used benchmark datasets and our own melanoma dataset. The results validate the effectiveness of our multimodal one-shot federated learning framework, which not only preserves data privacy and achieves high global model accuracy but also shows promise for future deployment in assisting dermatologists with melanoma detection of patients.

Federated learning has emerged as a widely adopted distributed machine learning paradigm. In the field of medical diagnosis, it has become a research hotspot, enabling multiple institutions to collaboratively leverage medical data for accurate analysis. However, the distributed nature of federated learning also introduces new challenges in data security and privacy protection, i.e., a curious server might collude with the client to infer private data of honest clients. In this paper, we implement our own FHE library and integrate it with several widely used federated learning methods, providing a unified framework. Our framework employs multiple-party Fully Homomorphic Encryption (FHE) to remove the requirements of a trusted third party or central key servers and address data security and privacy concerns in federated learning, ensuring that the data of honest participants is never exposed while maintaining the accuracy of the final analysis. We deploy our Privacy-preserving Federated Learning framework in the context of Melanoma Diagnosis, PFL-MD, and across multiple types of widely used benchmark datasets, our method achieves accuracy comparable to that of the original federated learning framework, thereby enabling reliable melanoma diagnosis. Extensive experiments further demonstrate the effectiveness of our framework.

Variable selection for genome-wide association studies (GWAS) has long been a central focus in academic research. However, with the advent of the big data era and the rapid growth of biomedical and healthcare data, scientists are increasingly challenged to extract meaningful information from massive datasets. Worse still, such data are often distributed across multiple parties, making collaborative analysis necessary while also requiring strong privacy preservation. To date, there is still no effective framework that can support high-dimensional data analysis, ensure data privacy across collaborators, and simultaneously capture the relatedness between explanatory and response variables. To address these challenges, we introduce the first high-performance computing framework for variable selection in GWAS with Vertical Federated Learning, termed VS-VFL. Our approach leverages Vertical Federated Learning to enable seamless multi-party collaboration while maintaining data privacy and security. Furthermore, we integrate a wide range of state-of-the-art methods, allowing collaborators to apply their preferred techniques, and we explicitly account for the noni.i.d. nature of biomedical data when analyzing the relatedness between explanatory and response variables. In addition, we employ novel optimization strategies and adaptive algorithms to efficiently handle high-dimensional data with sparse features. This framework empowers researchers to conduct comprehensive analyses and perform accurate linkage mapping of gene associations. Our framework is implemented in Python, can be easily deployed on any platform, and is designed to make advanced GWAS analysis accessible to a broader community of researchers.

Efficiently aggregating trained neural networks from local clients into a global model on a server is a widely researched topic in federated learning. Recently, motivated by diminishing privacy concerns, mitigating potential attacks, and reducing communication overhead, one-shot federated learning (i.e., limiting client-server communication into a single round) has gained popularity among researchers. However, the one-shot aggregation performances are sensitively affected by the non-identical training data distribution, which exhibits high statistical heterogeneity in some real-world scenarios. To address this issue, we propose a novel one-shot aggregation method with layer-wise posterior aggregation, named FedLPA. FedLPA aggregates local models to obtain a more accurate global model without requiring extra auxiliary datasets or exposing any private label information, e.g., label distributions. To effectively capture the statistics maintained in the biased local datasets in the practical non-IID scenario, we efficiently infer the posteriors of each layer in each local model using layer-wise Laplace approximation and aggregate them to train the global parameters. Extensive experimental results demonstrate that FedLPA significantly improves learning performance over state-of-the-art methods across several metrics.

Federated learning (FL) allows multiple clients to collaboratively learn a globally shared model through cycles of model aggregation and local model training, without the need to share data. Most existing FL methods train local models separately on different clients, and then simply average their parameters to obtain a centralized model on the server side. However, these approaches generally suffer from large aggregation errors and severe local forgetting, which are particularly bad in heterogeneous data settings. To tackle these issues, in this paper, we propose a novel FL framework that uses online Laplace approximation to approximate posteriors on both the client and server side. On the server side, a multivariate Gaussian product mechanism is employed to construct and maximize a global posterior, largely reducing the aggregation errors induced by large discrepancies between local models. On the client side, a prior loss that uses the global posterior probabilistic parameters delivered from the server is designed to guide the local training. Binding such learning constraints from other clients enables our method to mitigate local forgetting. Finally, we achieve state-of-the-art results on several benchmarks, clearly demonstrating the advantages of the proposed method.

In recent years, the demand for low latency and high reliability in industrial have been increasing rapidly. To meet these requirements, we propose a new decoding architecture of low-density parity-check convolutional codes (LDPC-CC). The proposed decoding architecture reduces the distance of the neighboring processors by using the rationale of compact pipeline decoder. At the same time, the stopping rule is employed in the process of iteration and the weighting factor is added to reduce the error propagation caused by the update of the parity-check node, thus accelerating the convergence of the decoding. The results show that the proposed algorithm has about 0.3 dB gain compared with the on-demand variable node activation (OVA) algorithm when the bit rate error (BER) is 10-4and maximum number of iterations is 10. Meanwhile, the initial delay and storage requirement are reduced by about 50% and the average number of iterations per decoded bit is also reduced largely.

Smart traffic control is a promising technology to exploit the available transportation resource and relieve traffic congestion in smart cities. This paper considers the bus scheduling problem, where a swarm of unmanned buses is supposed to intelligently accomplish tasks such as on-demand dynamic passenger pick-up or drop-off. Inspired by the dynamic window approach (DWA), To analyze this problem, the dynamic window approach (DWA) is adapted for the bus swarm scenario, and then an artificial sparrow model of neighborhood collaboration strategy is employed. Particularly, swarm coordination is realized by using a normalized Finsler geometric measured area instead of traditional Euclidean norm to coordinate the service area coverage among buses, which can help improve the local path planning of the bus swarm. The brain storm optimization (BSO) is further applied to enable the dynamic optimization and adjustment of bus swarm through analyzing the swarm motion, distribution, and the speed at which the individual reaches the target. Numerical results corroborate the effectiveness of the proposed detection and decision mechanism.