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
Applications and Challenges of Federated Learning Paradigm in the Big Data Era with Special Emphasis on COVID-19
TL;DR: This work describes the novel applications and challenges of the FL paradigm with special emphasis on the COVID-19 pandemic, and describes the synergies of FL with other emerging technologies to accomplish multiple services to fight the COIDS pandemic.
Proceedings ArticleDOI
Exploring Tradeoffs in Federated Learning on Serverless Computing Architectures
TL;DR: In this article , the authors explore the new tradeoffs presented by managing a federated learning task across heterogeneous resources and demonstrate these tradeoffs with experiments using a serverless federated Learning framework.
Journal ArticleDOI
Federated Learning with Dynamic Model Exchange
TL;DR: It was found that Federated Learning can achieve results on par with centralised learning and that the use of Differential Privacy can improve the robustness of the model against Membership Inference Attacks in an honest-but-curious setting.
Proceedings ArticleDOI
HARMONY: Heterogeneity-Aware Hierarchical Management for Federated Learning System
TL;DR: Harmony is proposed, a high-performance FL framework with heterogeneity-aware hierarchical management of training devices and training data that adopts a hierarchical structure to jointly handle both heterogeneities in a unified manner.
Journal ArticleDOI
SemiPFL: Personalized Semi-Supervised Federated Learning Framework for Edge Intelligence
TL;DR: This paper proposes a novel personalized semi-supervised federated learning (SemiPFL) framework to support edge users having no label or limited labeled datasets and a sizable amount of unlabeled data that is insufficient to train a well-performing model.
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