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
A survey on security and privacy of federated learning
Viraaji Mothukuri,Reza M. Parizi,Seyedamin Pouriyeh,Yan Huang,Ali Dehghantanha,Gautam Srivastava,Gautam Srivastava +6 more
TL;DR: This paper aims to provide a comprehensive study concerning FL’s security and privacy aspects that can help bridge the gap between the current state of federated AI and a future in which mass adoption is possible.
Journal ArticleDOI
Federated Learning for Internet of Things: A Comprehensive Survey
TL;DR: In this paper, a comprehensive survey of the emerging applications of federated learning in IoT networks is provided, which explores and analyzes the potential of FL for enabling a wide range of IoT services, including IoT data sharing, data offloading and caching, attack detection, localization, mobile crowdsensing and IoT privacy and security.
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A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection
TL;DR: A comprehensive review of federated learning systems can be found in this paper, where the authors provide a thorough categorization of the existing systems according to six different aspects, including data distribution, machine learning model, privacy mechanism, communication architecture, scale of federation and motivation of federation.
Journal ArticleDOI
Federated Learning for Internet of Things: A Comprehensive Survey
TL;DR: In this paper, a comprehensive survey of the emerging applications of federated learning in IoT networks is provided, which explores and analyzes the potential of FL for enabling a wide range of IoT services, including IoT data sharing, data offloading and caching, attack detection, localization, mobile crowdsensing and IoT privacy and security.
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Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challenges
TL;DR: The recent advances of federated learning towards enabling Federated learning-powered IoT applications are presented and a set of metrics such as sparsification, robustness, quantization, scalability, security, and privacy, is delineated in order to rigorously evaluate the recent advances.
References
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Stochastic Channel-Based Federated Learning for Medical Data Privacy Preserving.
TL;DR: A privacy-preserving method for the distributed system, Stochastic Channel-Based Federated Learning (SCBF), which enables the participants to train a high-performance model cooperatively without sharing their inputs and demonstrates that the saturating rate of performance could be promoted by introducing a pruning process.
Posted Content
Practical and Bilateral Privacy-preserving Federated Learning
TL;DR: This paper presents the first bilateral privacy-preserving federated learning scheme, which protects not only the raw training data of clients, but also model iterates during the training phase as well as final model parameters from leaking to untrusted clients and external attackers.
Proceedings ArticleDOI
Machine Learning for All: A More Robust Federated Learning Framework.
TL;DR: In this paper a general enhanced federated learning framework is presented and Homomorphic encryption algorithms are employed to enable model training on encrypted data.
Posted Content
Towards Federated Learning: Robustness Analytics to Data Heterogeneity.
TL;DR: This work studies Federated Learning of a classifier from data with edge device class distribution heterogeneity, and presents evidence, in both scenarios, that federated learning is robust to data heterogeneity.
Optimization in Federated Learning.
TL;DR: This paper presents the experiments on applying different popular optimization methods for training neural networks in a federated manner and discusses distributed optimization, a new paradigm in Machine Learning, that came into view with the spread of small user devices and applications written for them that can profit from ML.
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