Federated Learning: A Survey on Enabling Technologies, Protocols, and Applications.
TLDR
A more thorough summary of the most relevant protocols, platforms, and real-life use-cases of FL is provided to enable data scientists to build better privacy-preserved solutions for industries in critical need of FL.Abstract:
This paper provides a comprehensive study of Federated Learning (FL) with an emphasis on enabling software and hardware platforms, protocols, real-life applications and use-cases. FL can be applicable to multiple domains but applying it to different industries has its own set of obstacles. FL is known as collaborative learning, where algorithm(s) get trained across multiple devices or servers with decentralized data samples without having to exchange the actual data. This approach is radically different from other more established techniques such as getting the data samples uploaded to servers or having data in some form of distributed infrastructure. FL on the other hand generates more robust models without sharing data, leading to privacy-preserved solutions with higher security and access privileges to data. This paper starts by providing an overview of FL. Then, it gives an overview of technical details that pertain to FL enabling technologies, protocols, and applications. Compared to other survey papers in the field, our objective is to provide a more thorough summary of the most relevant protocols, platforms, and real-life use-cases of FL to enable data scientists to build better privacy-preserving solutions for industries in critical need of FL. We also provide an overview of key challenges presented in the recent literature and provide a summary of related research work. Moreover, we explore both the challenges and advantages of FL and present detailed service use-cases to illustrate how different architectures and protocols that use FL can fit together to deliver desired results.read more
Citations
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Toward Crowdsourced Transportation Mode Identification: A Semisupervised Federated Learning Approach
TL;DR: MTSSFL trains a deep neural network ensemble under a novel semisupervised FL framework, achieving highly accurate and privacy-protected crowdsourced TMI without depending on the availability of massive labeled data.
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Federated Learning and Its Role in the Privacy Preservation of IoT Devices
TL;DR: Federated learning (FL) as mentioned in this paper is a cutting-edge artificial intelligence approach that allows users to train using massive data using a secret confidentiality service, which incorporates machine learning (ML) training while removing data connections.
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Towards open and expandable cognitive AI architectures for large-scale multi-agent human-robot collaborative learning
TL;DR: A novel cognitive architecture for multi-agent LfD robotic learning is introduced in this paper, targeting to enable the reliable deployment of open, scalable and expandable robotic systems in large-scale and complex environments.
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Federated Learning for Edge Computing: A Survey
TL;DR: An overview of the methods used in Federated learning is provided with a focus on edge devices with limited computational resources and frameworks that are currently popular and that provide communication between clients and servers are presented.
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Fairness, integrity, and privacy in a scalable blockchain-based federated learning system
TL;DR: In this article, the authors proposed a federated machine learning (FL) system that incorporates blockchain technology, local differential privacy, and zero-knowledge proofs to achieve fairness, integrity, and privacy preservation.
References
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