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Enhancing Breast Cancer Classification in Histopathological Images through federated learning framework

IEEE ACCESS 2023年 11卷 61866-61880页

作者： Peta, Jyothi Koppu, Srinivas Vellore Inst Technol Sch Informat Technol & Engn Vellore 632014 Tamil Nadu India

In recent decades, the mortality rate of breast cancer in females is rapidly increasing because of unawareness and failed to detect in earlier stages. In existing, several studies are attempted to develop a robust mechanism for detecting breast cancers from the given input samples. However, they are not as much effective because of several limitations and the secured sharing of sensitive medical images is still a challenging problem faced by medical sector. Thus, the proposed study aims to introduce an automated disease diagnosis system using federated learning and deep learning which automates and speed up the process efficiently. The five crucial steps that involved in the proposed study are image acquisition, encryption, optimal key generation, secured data storing and disease classification. Initially, the required input medical images are gathered in the image acquisition stage. Then, to afford more confidentiality, the gathered medical samples are encrypted through an Extended ElGamal Image Encryption (E-EIE) method. Here, the efficiency of encryption process is enhanced by generating the suitable keys in optimal manner with the help of Improved Sand Cat Swarm Optimization (I-SCSO) algorithm. Next, the security of encrypted images are improvised by utilizing federated learning flower (FLF) framework for storage purpose. This framework has the ability to transmit the medical images with higher security. Finally, the stored images are decrypted and performs disease classification by using convolutional capsule twin attention tuna optimal network (C(2)T(2)Net) model. The available loss in the proposed classifier is reduced by fine-tuning the parameters using chaotic tuna swarm optimization (CTSO) algorithm. For simulation analysis, the proposed study used Python software and the experimental analysis is carried out by using BreakHis Database. The simulation results shows that the proposed study obtained higher performance in terms of accuracy (95.68%), recall (95

关键词： Breast cancer classification federated learning framework extended ElGamal image encryption improved sand cat swarm optimization convolutional capsule twin attention tuna optimal network

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Towards a federated learning framework on a Multi-Cloud Environment 34

Towards a Federated Learning Framework on a Multi-Cloud Envi...

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34th IEEE International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD)

作者： Brum, Rafaela C. Sens, Pierre Arantes, Luciana Castro, Maria Clicia Drummond, Lucia Maria de A. Fluminense Fed Univ Inst Comp Niteroi RJ Brazil Sorbonne Univ CNRS INRIA LIP6 Paris France Univ Estado Rio De Janeiro Inst Math & Stat Rio De Janeiro Brazil

ISBN: (数字)9781665451574

ISBN: (纸本)9781665451574

This paper proposes Multi-FedLS, a Cross-silo federated learning (FL) framework for a multi-cloud environment aiming at minimizing financial cost as well as execution time. It comprises four modules: Pre-Scheduling, Initial Mapping, Fault Tolerance, and Dynamic Scheduler. Given an application and a multi-cloud environment, the Pre-Scheduling module runs experiments to obtain the expected execution times of the FL tasks and communication delays. The Initial Mapping module receives these computed values and provides a scheduling map for the server and clients' VMs. Finally, Multi-FedLS deploys the selected VMs, starts the FL application, and monitors it. The Fault Tolerance (FT) module includes fault tolerance strategies in the FL application, such as checkpoint and replication, and detects some anomalous behaviors. In case of an unexpected increase in the communication delay or a VM failure, the FT module triggers the Dynamic Scheduler Module in order to select a new VM and resume the concerned tasks of the FL application. Some preliminary experiments are presented, confirming that some proposed strategies are crucial to efficiently execute an FL application on a multi-cloud environment.

关键词： federated learning framework Multi-Cloud Environment Efficient Resource Allocation

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Optimization and Implementation of Time Series Dimensionality Reduction Anti-fraud Model Integrating PCA and LSTM under the federated learning framework

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Procedia Computer Science 2025年 262卷 992-1001页

作者： Xuanrui Zhang College of Engineering University of California Berkeley Berkeley CA 94720 USA

For the problem of fraud detection in financial transaction systems, traditional anomaly detection methods based on time series often face challenges such as high computational overhead, inefficient feature extraction, and privacy and security risks when data distribution is highly heterogeneous. To this end, this paper integrates principal component analysis (PCA) and long short-term memory (LSTM) under the framework of federated learning (FL) to construct an efficient time series dimensionality reduction anti-fraud model to optimize fraud detection performance and protect data privacy. First, the distributed training of the federated learning framework is adopted to enable different data sources to perform feature extraction and model training locally, thereby achieving data sharing under the premise of protecting privacy. Then, PCA is used to extract the main feature components to improve the effectiveness of data representation. Subsequently, the LSTM model was introduced to perform deep learning on the features after dimensionality reduction to improve the ability to identify abnormal trading patterns. Finally, the federated Averaging (FedAvg) algorithm is used to aggregate the updates of each local model to optimize the global model performance, while the federated Differential Privacy (FDP) mechanism is used to enhance data privacy protection. In the experimental conclusion, the proposed model outperforms traditional methods on multiple standard datasets. On the research dataset, the accuracy of the proposed model reaches 93.6%, which is more advantageous than random forest (93.4%) and gradient boosting tree (92.5%). At the same time, the computational overhead and privacy protection capabilities of the model are well balanced. By applying a privacy protection mechanism, although the training time has increased, an effective trade-off between data privacy and performance has been achieved, providing researchers with a new solution.

关键词： federated learning framework Fraud Detection Privacy Protection PCA Dimensionality Reduction LSTM Model

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Design and Development of Server-Client Cooperation framework for federated learning 13

Design and Development of Server-Client Cooperation Framewor...

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13th International Conference on Ubiquitous and Future Networks (ICUFN)

作者： Park, Jongbin Kum, Seung Woo Korea Elect Technol Inst Informat Media Res Ctr Seoul South Korea

ISBN: (纸本)9781665485500

federated learning is a machine learning technique that enables distributed training without explicitly data sharing between multiple heterogeneous devices. In this paper, we propose and develop a practical federated learning framework to effectively support model deployment, aggregation, and client device monitoring. The proposed approach is designed as a microarchitecture service using container-related technologies such as Docker, Kubernetes, and Prometheus.

关键词： federated learning framework Edge Computing Micro Service Architecture

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Integration of federated learning and Graph Convolutional Networks for Movie Recommendation Systems

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Computers, Materials & Continua 2025年第5期83卷 2041-2057页

作者： Sony Peng Sophort Siet Ilkhomjon Sadriddinov Dae-Young Kim Kyuwon Park Doo-Soon Park Department of Software Convergence Soonchunhyang UniversityAsan31538Republic of Korea Department of Computer Software and Engineering Soonchunhyang UniversityAsan31538Republic of Korea AI⋅SW Education Institute Soonchunhyang UniversityAsan31538Republic of Korea

Recommendation systems(RSs)are crucial in personalizing user experiences in digital environments by suggesting relevant content or *** filtering(CF)is a widely used personalization technique that leverages user-item interactions to generate ***,it struggles with challenges like the cold-start problem,scalability issues,and data *** address these limitations,we develop a Graph Convolutional Networks(GCNs)model that captures the complex network of interactions between users and items,identifying subtle patterns that traditional methods may *** integrate this GCNs model into a federated learning(FL)framework,enabling themodel to learn fromdecentralized *** not only significantly enhances user privacy—a significant improvement over conventionalmodels but also reassures users about the safety of their ***,by securely incorporating demographic information,our approach further personalizes recommendations and mitigates the coldstart issue without compromising user *** validate our RSs model using the openMovieLens dataset and evaluate its performance across six key metrics:Precision,Recall,Area Under the Receiver Operating Characteristic Curve(ROC-AUC),F1 Score,Normalized Discounted Cumulative Gain(NDCG),and Mean Reciprocal Rank(MRR).The experimental results demonstrate significant enhancements in recommendation quality,underscoring that combining GCNs with CF in a federated setting provides a transformative solution for advanced recommendation systems.

关键词： Recommendation systems collaborative filtering graph convolutional networks federated learning framework

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Guest Editorial: Operationalizing Responsible AI

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第2期6卷 252-253页

作者： Lu, Qinghua Vassilev, Apostol Zhu, Jun Khomh, Foutse Data61 CSIRO EveleighNSW2015 Australia NIST Gaithersburg MD29899 United States Tsinghua University Beijing100084 China Polytechnique Montréal MontrealQCH3T 0A3 Canada

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federated Machine learning for Detection of Skin Diseases and Enhancement of Internet of Medical Things (IoMT) Security

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2023年第2期27卷 835-841页

作者： Hossen, Md. Nazmul Panneerselvam, Vijayakumari Koundal, Deepika Ahmed, Kawsar Bui, Francis M. Ibrahim, Sobhy M. Mawlana Bhashani Sci & Technol Univ Dept Informat & Commun Technol Tangail 1902 Bangladesh SIMATS Inst ECE Saveetha Sch Engn Dept Appl Elect Chennai 602105 India Univ Petr Dept Syst Energy Studies Dehra Dun 248006 India Univ Petr Sch Comp Sci Energy Studies Dehra Dun 248006 India Univ Saskatchewan Dept Elect & Comp Engn Saskatoon SK S7N 5A9 Canada MBSTU Dept ICT Grp Biophotomati? Tangail 1902 Bangladesh Univ Saskatchewan Dept Elect & Comp Engn Saskatoon SK S7N 5A9 Canada King Saud Univ Coll Sci Dept Biochem Riyadh 11451 Saudi Arabia

Human skin disease, the most infectious dermatological ailment globally, is initially diagnosed by sight. Some clinical screening and dermoscopic analysis of skin biopsies and scrapings for accurate classification are medically compulsory. Classification of skin diseases using medical images is more challenging because of the complex formation and variant colors of the disease and data security concerns. Both the Convolution Neural Network (CNN) for classification and a federated learning approach for data privacy preservation show significant performance in the realm of medical imaging fields. In this paper, a custom image dataset was prepared with four classes of skin disease, a CNN model was suggested and compared with several benchmark CNN algorithms, and an experiment was carried out to ensure data privacy using a federated learning approach. An image augmentation strategy was followed to enlarge the dataset and make the model more general. The proposed model achieved a precision of 86%, 43%, and 60%, and a recall of 67%, 60%, and 60% for acne, eczema, and psoriasis. In the federated learning approach, after distributing the dataset among 1000, 1500, 2000, and 2500 clients, the model showed an average accuracy of 81.21%, 86.57%, 91.15%, and 94.15%. The CNN-based skin disease classification merged with the federated learning approach is a breathtaking concept to classify human skin diseases while ensuring data security.

关键词： Diseases Skin Collaborative work Convolutional neural networks Computational modeling Data models Convolution Benchmark algorithms convolution neural network federated learning framework IoMT security medical imaging skin disease classification

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federated learning-Driven Decentralized Intelligence for Explainable Anomaly Detection in Industrial Operations 2

Federated Learning-Driven Decentralized Intelligence for Exp...

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2nd International Conference on Sustainable Computing and Smart Systems (ICSCSS)

作者： Marry, Prabhakar Mounika, Y. Nanditha, S. Shiva, R. Saikishore, R. Vignan Inst Technol & Sci A Dept IT Hyderabad India

ISBN: (纸本)9798350391558;9798350379990

In today's rapidly evolving landscape of smart industry, timely detection of anomalies within industrial machinery stands as a critical factor in preventing breakdowns and upholding safety standards. While Machine learning (ML) offers an automated solution, leveraging the Internet of Things (IoT) for data collection, it encounters challenges like data security risks and uneven data distribution. To overcome these challenges, this study proposes an innovative Autoencoder-based federated learning framework. This framework conducts multiple rounds of training on sensor data gathered from randomly selected IoT devices. This study aims to experiment with model aggregation techniques such as FedAvg and FedAvgM, followed by a thorough comparative analysis. Furthermore, by integrating Explainable AI techniques, we enhance the transparency and interpretability of our anomaly detection models, providing stakeholders with insightful explanations of their functioning.

关键词： Anomaly detection federated learning framework Industrial Machinery

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Collaborative Algorithm for User Trust and Data Security Based on Blockchain and Machine learning

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Procedia Computer Science 2025年 262卷 757-765页

作者： Dishu Yang Xingyu Liu Khoury College of Computer Sciences Northeastern University San Jose CA 95113 United States Social Science Research Institute Duke University Durham NC 27708 United States

Machine learning has achieved remarkable results in numerous fields, demonstrating strong momentum and promising prospects for future development. However, machine learning is facing issues related to data security. User data contains a vast amount of sensitive personal information, and once privacy is breached, users may not only suffer from harassment but also face threats to their lives and property security. As a result, users’ willingness and trust in sharing local raw data are gradually decreasing. In response to this situation, federated learning technology has emerged, which enables efficient training of decentralized data through distributed machine learning methods while protecting users’ data privacy. Traditional federated learning systems suffer from issues such as single points of failure and lack of trust. Blockchain, as a decentralized, traceable, and tamper-resistant distributed ledger technology, provides a new solution for federated learning. It records every update of the global model, verifies and tracks local updates, and is equipped with a fair incentive mechanism. Based on these ideas, this paper proposes a federated learning framework combined with blockchain, aiming to address data security issues in federated learning.

关键词： Machine learning Blockchain Data Security federated learning framework

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