Assume that there is a set of "voters" and a set of "candidates", where each voter assigns a numerical score to each candidate. There is a scoring function (such as the mean or the median), and a c...
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ISBN:
(纸本)9781450341912
Assume that there is a set of "voters" and a set of "candidates", where each voter assigns a numerical score to each candidate. There is a scoring function (such as the mean or the median), and a consensus ranking is obtained by applying the scoring function to each candidate's scores. The problem is to find the top k candidates, while minimizing the number of database accesses. The speaker will present an algorithm that is optimal in an extremely strong sense: not just in the worst case or the average case, but in every case! Even though the algorithm is only 10 lines long (!), the paper containing the algorithm won the 2014 Godel Prize, the top prize for a paper in theoretical computer science.
The aim of heterogeneous federated learning (HFL) is to address the issues of data heterogeneity, computational resource disparity, and model generalizability and security in federated learning (FL). To facilitate the...
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The aim of heterogeneous federated learning (HFL) is to address the issues of data heterogeneity, computational resource disparity, and model generalizability and security in federated learning (FL). To facilitate the collaborative training of data and enhance the predictive performance of models, a heterogeneous federated learning algorithm based on contribution-weighted aggregation (HFedCWA) is proposed in this paper. First, weights are assigned on the basis of the distribution differences and quantities of heterogeneous device data, and a contribution-based weighted aggregation method is introduced to dynamically adjust weights and balance data heterogeneity. Second, personalized strategies based on regularization are formulated for heterogeneous devices with different weights, enabling each device to participate in the overall task in an optimal manner. Differential privacy methods are concurrently utilized in FL training to further enhance the security of the system. Finally, experiments are conducted under various data heterogeneity scenarios using the MNIST and CIFAR10 datasets, and the results demonstrate that the HFedCWA can effectively improve the model's generalizability ability and adaptability to heterogeneous data, thereby enhancing the overall efficiency and performance of the HFL system.
In the realms of robotics and the autonomous driving field, deep learning has been a game-changer for applications such as perception, obstacle avoidance, SLAM, etc. The success of these deep learning algorithms, howe...
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The expansion of data has prompted the creation of various NoSQL (Not only SQL) databases, including graph -oriented databases, which provide an understandable abstraction for modeling complex domains and managing hig...
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The expansion of data has prompted the creation of various NoSQL (Not only SQL) databases, including graph -oriented databases, which provide an understandable abstraction for modeling complex domains and managing highly connected data. However, to add graph data to existing decision sup-port systems, new data warehouse systems that consider the special characteristics of graphs need to be developed. This work proposes a novel method for creating a data warehouse under a graph database and demonstrates how OLAP (Online Analytical Processing) structures created for reporting can be handled by graph databases. Additionally, the paper suggests using aggregation algorithms based association rules techniques to improve the efficiency of reporting and data analysis within a graph-based data warehouse. Finally, we provide a Cypher language implementation of the suggested approach to evaluate and validate our approach.
Protecting user privacy is essential in machine learning research, especially in the context of data collection. Federated learning (FL), which trains models across decentralized devices without sharing raw data, has ...
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Protecting user privacy is essential in machine learning research, especially in the context of data collection. Federated learning (FL), which trains models across decentralized devices without sharing raw data, has emerged as a promising solution. However, FL is still vulnerable to security threats, including inference attacks, which have been underexplored in comparison to poisoning and backdoor attacks that have received more attention in existing research. To address these vulnerabilities, this paper proposes a novel aggregation framework called homomorphic and polymorphic federated learning aggregation of parameters (HP_FLAP). HP_FLAP integrates both homomorphic and polymorphic encryption to enhance the security and privacy of FL. Homomorphic encryption allows the server to perform aggregation on encrypted parameters without decrypting them, ensuring that sensitive information is protected during the aggregation process. Polymorphic encryption further strengthens security by using different encryption keys for each set of parameters, mitigating the risk of system-wide compromise in case a key is leaked. This dual encryption approach effectively counters inference attacks while maintaining robust protections against other security threats. The framework is evaluated using multiple models, including logistic regression, Gaussian Naive Bayes, Stochastic Gradient Descent, and Multi-Layer Perceptron, demonstrating HP_FLAP's ability to enhance both security and privacy in FL environments.
Federated learning (FL)–a distributed machine learning that offers collaborative training of global models across multiple clients. FL has been considered for the design and development of many FL systems in various ...
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Federated learning (FL)–a distributed machine learning that offers collaborative training of global models across multiple clients. FL has been considered for the design and development of many FL systems in various domains. Hence, we present a comprehensive survey and analysis of existing FL systems, drawing insights from more than 250 articles published in 2019-2024. Our review elucidates the functioning of FL systems, particularly in comparison with alternative distributed learning approaches. Considering the healthcare domain as an example, we define the building blocks of a typical FL healthcare system, including system architecture, federation scale, data partitioning, open-source frameworks, ML models, and aggregation algorithms. Furthermore, we identify and discuss key challenges associated with the design and implementation of FL systems within the healthcare sector while outlining the directions of future research. In general, through systematic categorization and analysis of existing FL systems, we offer insights to design efficient, accurate, and privacy-preserving healthcare applications using cutting-edge FL techniques.
Federated learning (FL), with the aim of training machine learning models using data and computational resources on edge devices without sharing raw local data, is essential for improving agricultural management and s...
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Federated learning (FL), with the aim of training machine learning models using data and computational resources on edge devices without sharing raw local data, is essential for improving agricultural management and smart agriculture. This study is a review of FL applications that address various agricultural problems. We compare the types of data partitioning and types of FL (horizontal partitioning and horizontal FL, vertical partitioning and vertical FL, and hybrid partitioning and transfer FL), architectures (centralized and decentralized), levels of federation (cross-device and cross-silo), and the use of aggregation algorithms in different reviewed approaches and applications of FL in agriculture. We also briefly review how the communication challenge is solved by different approaches. This work is useful for gaining an overview of the FL techniques used in agriculture and the progress made in this field.
Machine learning (ML) has succeeded in improving our daily routines by enabling automation and improved decision making in a variety of industries such as healthcare, finance, and transportation, resulting in increase...
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Machine learning (ML) has succeeded in improving our daily routines by enabling automation and improved decision making in a variety of industries such as healthcare, finance, and transportation, resulting in increased efficiency and production. However, the development and widespread use of this technology has been significantly hampered by concerns about data privacy, confidentiality, and sensitivity, particularly in healthcare and finance. The "data hunger" of ML describes how additional data can increase performance and accuracy, which is why this question arises. Federated learning (FL) has emerged as a technology that helps solve the privacy problem by eliminating the need to send data to a primary server and collect it where it is processed and the model is trained. To maintain privacy and improve model performance, FL shares parameters rather than data during training, in contrast to the typical ML practice of sending user data during model development. Although FL is still in its infancy, there are already applications in various industries such as healthcare, finance, transportation, and others. In addition, 32% of companies have implemented or plan to implement federated learning in the next 12-24 months, according to the latest figures from KPMG, which forecasts an increase in investment in this area from USD 107 million in 2020 to USD 538 million in 2025. In this context, this article reviews federated learning, describes it technically, differentiates it from other technologies, and discusses current FL aggregation algorithms. It also discusses the use of FL in the diagnosis of cardiovascular disease, diabetes, and cancer. Finally, the problems hindering progress in this area and future strategies to overcome these limitations are discussed in detail.
Data collection and processing in real time is one of the most challenging domains for big data. The sustainable proliferation of unbounded streaming data has become arduous for data collection, data pre-process, data...
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In Wireless sensor networks, Data aggregation are vital techniques to attain potential power within the sensing element network. In some application such as: wireless sensing element network, data processing, cloud co...
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ISBN:
(纸本)9781467393379
In Wireless sensor networks, Data aggregation are vital techniques to attain potential power within the sensing element network. In some application such as: wireless sensing element network, data processing, cloud computing data aggregation is widely used. As the sensor nodes are battery driven, efficient power utilization is necessary to reduce the compromised nodes and traffic thereby reducing the data sent to base station by enhancing the network lifespan. A challenge to data aggregation is however to secure aggregative information from compromise node attacks and revealing throughout aggregating method to obtain exact aggregative results.
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