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35th ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems (PODS)

作者： Fagin, Ronald IBM Res Almaden San Jose CA 95120 USA

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.

关键词： aggregation algorithms score aggregation instance optimal

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HFedCWA: heterogeneous federated learning algorithm based on contribution-weighted aggregation

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APPLIED INTELLIGENCE 2025年第2期55卷 1-16页

作者： Du, Jiawei Wang, Huaijun Li, Junhuai Wang, Kan Fei, Rong Xian Univ Technol Sch Comp Sci & Engn 5 Jinhua South Rd Xian 710048 Shaanxi Peoples R China

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.

关键词： Federated learning Data heterogeneity aggregation algorithms Personalized methods Differential privacy

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aggregation Mechanisms in Federated Learning for Enhancing Robotic Visual Obstacle Avoidance 5

Aggregation Mechanisms in Federated Learning for Enhancing R...

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5th International Conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2023

作者： Zhang, Haizhou Cheng, Qianyu Chen, Xiong School of Information Science and Technology Fudan University Shanghai China City University of HongKong Department of Computer Science Hong Kong Hong Kong

ISBN: (纸本)9798350357950

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, however, relies on large-scale data sets for training, raising substantial privacy concerns for users unwilling to share raw data. Addressing this issue, our study explores federated learning in robotics to leverage its inherent privacy-protection attributes. Federated learning, a privacy-conscious method within the machine learning paradigm, has become increasingly prominent in recent years. More importantly, an essential aspect of our work is the assessment of diverse aggregation mechanisms within the federated learning framework, aiming at enhancing robotic functionality. We gather data and implement a federated learning model using various aggregation algorithms in visual obstacle avoidance contexts. The results demonstrate considerable variances in the performance of these algorithms, indicating that certain federated learning aggregation methods can exceed the performance of traditional centralized learning models. This study affirms the feasibility and advantages of federated learning in robotic applications, publicly available on github. © 2023 IEEE.

关键词： aggregation algorithms autonomous robots federated learning privacy protection vision-based obstacle avoidance

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A Review of Federated Learning in Agriculture

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SENSORS 2023年第23期23卷 9566页

作者： Zalik, Krista Rizman Zalik, Mitja Univ Maribor Fac Elect Engn & Comp Sci Maribor 2000 Slovenia Univ Maribor Fac Nat Sci & Math Maribor 2000 Slovenia

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.

关键词： federated learning agriculture architecture data partitioning federation scale aggregation algorithms communication bottleneck

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Reviewing Federated Machine Learning and Its Use in Diseases Prediction

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SENSORS 2023年第4期23卷 2112-2112页

作者： Moshawrab, Mohammad Adda, Mehdi Bouzouane, Abdenour Ibrahim, Hussein Raad, Ali Univ Quebec Rimouski Dept Math Informat & Genie 300 Allee Ursulines Rimouski PQ G5L 3A1 Canada Univ Quebec Chicoutimi Dept Informat & Math 555 Blvd Univ Chicoutimi PQ G7H 2B1 Canada Inst Technol Maintenance Ind 175 Rue Verendrye Sept Iles PQ G4R 5B7 Canada Islamic Univ Lebanon Fac Arts & Sci POB 30014 Wardaniyeh 30014 Lebanon

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.

关键词： federated machine learning federated learning privacy preservation aggregation algorithms diseases prediction cardiovascular diseases diabetes cancer smart wearables smart health

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New Approach based on Association Rules for Building and Optimizing OLAP Cubes on Graphs

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2023年第7期14卷 997-1008页

作者： Labzioui, Redouane Letrache, Khadija Ramdani, Mohammed Mohammedia Univ Hassan II Fac Sci & Tech Informat Dept LIM Lab Casablanca Morocco

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.

关键词： NoSQL graph-oriented databases data warehouse OLAP aggregation algorithms association rules cypher language

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A Survey of Real-Time Big Data Processing algorithms 1st

A Survey of Real-Time Big Data Processing Algorithms

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1st International and 4th National Conference on Reliability and Safety Engineering, INCRS 2018

作者： Lal, Devesh Kumar Suman, Ugrasen School of Computer Science & IT Devi Ahilya University Indore India

ISBN: (纸本)9789811537455

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 optimization, etc. Real-time streaming for data collection can effectively be performed by windowing mechanism. In this communication, we have discussed various windowing mechanisms such as sliding window, tumbling window, landmark window, index-based window, adaptive size tumbling window, and partitioned-based window. The reliability measure, which depends upon selection of appropriate windowing mechanism, has also been discussed. These window-based algorithms have been compared on the basis of CPU utilization, memory consumption, time efficiency, and operation compatibility. In this paper, we have surveyed various aggregation algorithms such as reactive aggregator, flatFAT, flatFIT, B-Int, DABA, and two stacks aggregator and compared them based on time complexity. Remarkably, a hybrid window mechanism has been introduced in this study which can handle the most recent data stream and variable rate of data stream by sliding window and tumbling window, respectively. © Springer Nature Singapore Pte Ltd. 2020.

关键词： aggregation algorithms Big data Real-time data processing Streaming data Window algorithms

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Enhanced Data aggregation Techniques for Compromised Node Attacks in Wireless Sensor Networks

Enhanced Data Aggregation Techniques for Compromised Node At...

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IEEE International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET)

作者： Krithika, S. Preshiya, D. Jyothi Sathyabama Univ Dept Appl Elect Madras Tamil Nadu India

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.

关键词： sensor networks aggregation algorithms detection compromise network lifespan

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Can belief structures improve our understanding of safety climate survey data?

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2015年 66卷 103-118页

作者： Turcanu, Catrinel Mkrtchyan, Lusine Nagy, Ahmed Faure, Pierre CEN SCK Belgian Nucl Res Ctr B-2400 Mol Belgium Ecole Mines Nantes Nantes France

Questionnaire-based surveys are a standard method used for assessing the safety climate within an organization. However, their analysis - in particular data aggregation - poses several challenges, among which are subjective judgment, incompleteness and uncertainty. This paper explores the use of approaches based on belief structures for aggregating data from safety climate questionnaires. Data relevant to this study were collected through a questionnaire administered to the employees of a nuclear research centre. The results show that, while belief structures may offer a promising way to represent data collected from questionnaires, the existing aggregation methods are not always adequate. Averaging schemes applied to belief structures seem the most suited - among the methods investigated - in the specific problem context analyzed. The analysis of the survey data shows the limitations of quantitative approaches for safety culture assessment and the need to always complement these with in-depth qualitative analysis. (C) 2015 Elsevier Inc. All rights reserved.

关键词： Safety climate Questionnaire Belief structures aggregation algorithms

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Peer-to-peer aggregation techniques dissected

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INTERNATIONAL JOURNAL OF PARALLEL EMERGENT AND DISTRIBUTED SYSTEMS 2010年第1期25卷 51-71页

作者： Ogston, Elth Jarvis, Stephen A. Univ Warwick Dept Comp Sci Coventry W Midlands England

aggregation is the process of gathering and combining information from a number of sources. In peer-to-peer systems, aggregation is a basic component of a range of applications, including monitoring and complex-query resolution. Peer-to-peer aggregation services themselves are dependent on a number of other fundamental peer-to-peer services - directories, multicasting and system-size approximation. The overall performance characteristics of an aggregation service are affected by the chosen implementation method for these underlying services. To illustrate this relationship, aggregation techniques for internet-based peer-to-peer systems are surveyed and dissected into their component parts. We further consider the problem of running one-off aggregation queries in a peer-to-peer network. A new aggregation service, Bliksum, which uses a novel combination of underlying services, is introduced. Bliksum employs unstructured peer-to-peer techniques for node sampling, multicasting and system-size approximation, in combination with a method of building a temporary tree structure for aggregation itself. Unstructured peer-to-peer techniques have been shown to be highly resilient to node churn, avoiding the problem inherent in structured systems of maintaining the desired structure when the set of nodes changes rapidly. We present experiments showing that Bliksum retains these advantages while reducing communications cost and reducing information loss compared to pure gossip-based aggregation.

关键词： peer-to-peer systems aggregation algorithms gossiping

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