Federated Learning: A Distributed Shared Machine Learning Method

Hu, Kai; Li, Yaogen; Xia, Min; Wu, Jiasheng; Lu, Meixia; Zhang, Shuai; Weng, Liguo

doi:10.1155/2021/8261663

Cited by 30 publications

(18 citation statements)

References 32 publications

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“…Federated learning is a distributed machine learning framework [ 4 , 5 ], which aims to solve the problems of user privacy and data islands which occur in the process of machine learning. Without data transmission, this method can train a machine learning model through using data from various devices.…”

Section: Literature Reviewmentioning

confidence: 99%

Design of Sponsored Search Auction Mechanism for Federated Learning Advertising Platform

Hong

Cui

Yang

2022

Computational Intelligence and Neuroscience

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Federated learning has demonstrated strong capabilities in terms of addressing concerns related to data islands and privacy protection. However, in real application scenarios, participants in federated learning have difficulty matching. For example, two companies distributed in different regions do not know that the other party also needs federated learning in the case of information asymmetry. Therefore, it is difficult to build alliances. To enable suppliers and consumers to find one or more federated learning objects that are relatively satisfactory in a short time, this paper considers the idea of establishing a federated learning advertising platform, where data transactions need to consider privacy protection. A sponsored search auction mechanism design method is introduced to solve the problem of ranking the presentation order of participant advertisements. Due to the potential malicious bidding problem, which occurs when using the classic sponsored search auction mechanism under the federated learning scenario, this paper proposes a novel federated sponsored search auction mechanism based on the Myerson theorem, improving upon the ranking index used in the classic sponsored search auction mechanism. A large number of experimental results on a simulation data set show that our proposed method can fairly select and rank the data providers participating in the bidding. Compared with other benchmark mechanisms, the malicious bidding rate is significantly decreased. In the long run, the proposed mechanism can encourage more data providers to participate in the federated learning platform, thus continuously promoting the establishment of a federated learning ecosystem.

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Section: Literature Reviewmentioning

confidence: 99%

Design of Sponsored Search Auction Mechanism for Federated Learning Advertising Platform

Hong

Cui

Yang

2022

Computational Intelligence and Neuroscience

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“…5) Calculate the average of the update values and apply it to the overall model. 6) Repetition of steps 2 through 5 FL has been used, for instance, to train prediction models for mobile keyboards without sending confidential typing information to servers [91]. FedCV is a benchmarking system and federated learning library that assesses FL on the three most common computer vision tasks, including object identification, image segmentation, and classification.…”

Section: Federated Learningmentioning

confidence: 99%

“…FL differs from distributed ML in that the data that each participant uploads to the server is a trained sub-model rather than the original data. The FL also permits asynchronous transmission at the same time, allowing for a suitable reduction in the communication needs [91]. Each unit builds a model and transmits its input data to the server for aggregation.…”

Section: Federated Learningmentioning

confidence: 99%

A Review on Machine Learning Styles in Computer Vision—Techniques and Future Directions

et al. 2022

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Computer applications have considerably shifted from single data processing to machine learning in recent years due to the accessibility and availability of massive volumes of data obtained through the internet and various sources. Machine learning is automating human assistance by training an algorithm on relevant data. Supervised, Unsupervised, and Reinforcement Learning are the three fundamental categories of machine learning techniques. In this paper, we have discussed the different learning styles used in the field of Computer vision, Deep Learning, Neural networks, and machine learning. Some of the most recent applications of machine learning in computer vision include object identification, object classification, and extracting usable information from images, graphic documents, and videos. Some machine learning techniques frequently include zero-shot learning, active learning, contrastive learning, self-supervised learning, life-long learning, semi-supervised learning, ensemble learning, sequential learning, and multi-view learning used in computer vision until now. There is a lack of systematic reviews about all learning styles. This paper presents literature analysis of how different machine learning styles evolved in the field of Artificial Intelligence (AI) for computer vision. This research examines and evaluates machine learning applications in computer vision and future forecasting. This paper will be helpful for researchers working with learning styles as it gives a deep insight into future directions.

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“…FL can be an appropriate choice to address the issues encountered with the traditional ML-based IDSs. FL is one of the most adaptable techniques that allow the training of ML algorithms on edge devices [16]. FL approach enables multiple participants to develop robust and efficient ML models without data sharing.…”

Section: Motivationmentioning

confidence: 99%

A federated learning framework for cyberattack detection in vehicular sensor networks

Driss

Almomani

Huma

et al. 2022

Complex Intell. Syst.

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Vehicular Sensor Networks (VSN) introduced a new paradigm for modern transportation systems by improving traffic management and comfort. However, the increasing adoption of smart sensing technologies with the Internet of Things (IoT) made VSN a high-value target for cybercriminals. In recent years, Machine Learning (ML) and Deep Learning (DL) techniques attracted the research community to develop security solutions for IoT networks. Traditional ML and DL approaches that operate with data stored on a centralized server raise major privacy problems for user data. On the other hand, the resource-constrained nature of a smart sensing network demands lightweight security solutions. To address these issues, this article proposes a Federated Learning (FL)-based attack detection framework for VSN. The proposed scheme utilizes a group of Gated Recurrent Units (GRU) with a Random Forest (RF)-based ensembler unit. The effectiveness of the suggested framework is investigated through multiple performance metrics. Experimental findings indicate that the proposed FL approach successfully detected the cyberattacks in VSN with the highest accuracy of 99.52%. The other performance scores, precision, recall, and F1 are attained as 99.77%, 99.54%, and 99.65%, respectively.

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Federated Learning: A Distributed Shared Machine Learning Method

Cited by 30 publications

References 32 publications

Design of Sponsored Search Auction Mechanism for Federated Learning Advertising Platform

Design of Sponsored Search Auction Mechanism for Federated Learning Advertising Platform

A Review on Machine Learning Styles in Computer Vision—Techniques and Future Directions

A federated learning framework for cyberattack detection in vehicular sensor networks

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