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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Journal ArticleDOI
Analysis of Privacy-Enhancing Technologies in Open-Source Federated Learning Frameworks for Driver Activity Recognition
TL;DR: The experiments showed that the current implementation of the privacy-preserving techniques in open-source FL frameworks limits the practical application of FL to cross-silo settings.
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Modified Artificial Bee Colony Based Feature Optimized Federated Learning for Heart Disease Diagnosis in Healthcare
M. M. Yaqoob,Muhammad Nazir,Abdullah Yousafzai,Muhammad Amir Khan,Asadullah Shaikh,Abeer D. Algarni,Hela Elmannai +6 more
TL;DR: In this article , the authors proposed a framework based on federated matched averaging with a modified Artificial Bee Colony (M-ABC) optimization algorithm to overcome privacy issues and to improve the diagnosis method for the prediction of heart disease.
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Aggregation Strategy on Federated Machine Learning Algorithm for Collaborative Predictive Maintenance
Ali Bemani,Niclas Björsell +1 more
TL;DR: This study describes distributed ML for PM applications and proposes two federated algorithms: Federated support vector machine (FedSVM) with memory for anomaly detection and federated long-short term memory (FedLSTM) for remaining useful life (RUL) estimation that enables factories at the fog level to maximize their PM models’ accuracy without compromising their privacy.
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Privacy-preserving federated learning for scalable and high data quality computational-intelligence-as-a-service in Society 5.0
TL;DR: In this article , a blockchain-based decentralized federated learning framework for secure, scalable, and privacy-preserving computational intelligence, called Decentralized Computational Intelligence as a Service (DCIaaS), is proposed.
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Recommender Systems Based on Graph Embedding Techniques: A Review
TL;DR: In this article , the authors systematically retrospected graph embedding-based recommendation from embedding techniques for bipartite graphs, general graphs and knowledge graphs, and proposed a general design pipeline of that.
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