Federated Learning (FL) has been foundational in improving the performance of a wide range of applications since it was first introduced by Google. Some of the most prominent and commonly used FL-powered applications are Android’s Gboard for predictive text and Google Assistant. FL can be defined as a setting that makes on-device, collaborative Machine Learning possible. A wide range of literature has studied FL technical considerations, frameworks, and limitations with several works presenting a survey of the prominent literature on FL. However, prior surveys have focused on technical considerations and challenges of FL, and there has been a limitation in more recent work that presents a comprehensive overview of the status and future trends of FL in applications and markets. In this survey, we introduce the basic fundamentals of FL, describing its underlying technologies, architectures, system challenges, and privacy-preserving methods. More importantly, the contribution of this work is in scoping a wide variety of FL current applications and future trends in technology and markets today. We present a classification and clustering of literature progress in FL in application to technologies including Artificial Intelligence, Internet of Things, blockchain, Natural Language Processing, autonomous vehicles, and resource allocation, as well as in application to market use cases in domains of Data Science, healthcare, education, and industry. We discuss future open directions and challenges in FL within recommendation engines, autonomous vehicles, IoT, battery management, privacy, fairness, personalization, and the role of FL for governments and public sectors. By presenting a comprehensive review of the status and prospects of FL, this work serves as a reference point for researchers and practitioners to explore FL applications under a wide range of domains. 

Federated learning review: Fundamentals, enabling technologies, and future applications

The rise in popularity of Internet of Things (IoT) devices has attracted hackers to develop IoT-specific attacks. The microservice architecture of IoT devices relies on the Internet to provide their intended services. An unguarded IoT network makes inter-connected devices vulnerable to attacks. It will be a tedious and ineffective process to manually detect the attacks in the network, as the attackers frequently upgrade their attack strategies. Machine learning (ML)-assisted approaches have been proposed to build intrusion detection for cybersecurity automation in IoT networks. However, most such approaches focus on training an ML model using a single view of the dataset, which often fails to build insightful knowledge and understand each feature’s impact on the ML model’s decision-making ability. As such, the model training with a single view may result in an incomplete understanding of patterns in large feature-set datasets. Moreover, the current approaches are mainly designed in a centralized manner in which the raw data is transferred from the edge devices to the central server for training. This, in turn, may expose the data to all kinds of attacks without adhering to the privacy-preserving of data security. Multi-view learning has gained popularity for its ability to learn from different data views and deliver efficient performance with more distinguished predictions. This paper proposes a federated learning-based intrusion detection approach, called MV-FLID, that trains on multiple views of IoT network data in a decentralized format to detect, classify, and defend against attacks. The multi-view ensemble learning aspect helps in maximizing the learning efficiency of different classes of attacks. The Federated Learning (FL) aspect, wherein the device’s data is not shared to the server, performs profile aggregation efficiently with the benefit of peer learning. Our evaluation results show that our proposed approach has higher accuracy compared to the traditional non-FL centralized approach.

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An Ensemble Multi-View Federated Learning Intrusion Detection for IoT

In this paper we introduce “Federated Learning Utilities and Tools for Experimentation” (FLUTE), a high-performance open source platform for federated learning research and ofﬂine simulations. The goal of FLUTE is to enable rapid prototyping and simulation of new federated learning algorithms at scale, including novel optimization, privacy, and communications strategies. We describe the architecture of FLUTE, enabling arbitrary federated modeling schemes to be realized. We compare the platform with other state-of-the-art platforms and describe available features of FLUTE for experimentation in core areas of active research, such as optimization, privacy, and scalability. A comparison with other established platforms shows speed-ups of up to 42 × and savings in memory footprint of 3 × . A sample of the platform capabilities is also presented for a range of tasks, as well as other functionality, such as linear scaling for the number of participating clients, and a variety of federated optimizers, including FedAdam, DGA, etcetera .

FLUTE: A Scalable, Extensible Framework for High-Performance Federated Learning Simulations

The incredible development of federated learning (FL) has benefited various tasks in the domains of computer vision and natural language processing, and the existing frameworks such as TFF and FATE has made the deployment easy in real-world applications. However, federated graph learning (FGL), even though graph data are prevalent, has not been well supported due to its unique characteristics and requirements. The lack of FGL-related framework increases the efforts for accomplishing reproducible research and deploying in real-world applications. Motivated by such strong demand, in this paper, we first discuss the challenges in creating an easy-to-use FGL package and accordingly present our implemented package FederatedScope-GNN (FS-G), which provides (1) a unified view for modularizing and expressing FGL algorithms; (2) comprehensive DataZoo and ModelZoo for out-of-the-box FGL capability; (3) an efficient model auto-tuning component; and (4) off-the-shelf privacy attack and defense abilities. We validate the effectiveness of FS-G by conducting extensive experiments, which simultaneously gains many valuable insights about FGL for the community. Moreover, we employ FS-G to serve the FGL application in real-world E-commerce scenarios, where the attained improvements indicate great potential business benefits. We publicly release FS-G, as submodules of FederatedScope, at https://github.com/alibaba/FederatedScope to promote FGL's research and enable broad applications that would otherwise be infeasible due to the lack of a dedicated package.

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FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Package for Federated Graph Learning

Although remarkable progress has been made by existing federated learning (FL) platforms to provide infrastructures for development, these platforms may not well tackle the challenges brought by various types of heterogeneity. To fill this gap, in this paper, we propose a novel FL platform, named FederatedScope, which employs an event-driven architecture to provide users with great flexibility to independently describe the behaviors of different participants. Such a design makes it easy for users to describe participants with various local training processes, learning goals and backends, and coordinate them into an FL course with synchronous or asynchronous training strategies. Towards an easy-to-use and flexible platform, FederatedScope enables rich types of plug-in operations and components for efficient further development, and we have implemented several important components to better help users with privacy protection, attack simulation and auto-tuning. We have released FederatedScope at https://github.com/alibaba/FederatedScope to promote academic research and industrial deployment of federated learning in a wide range of scenarios.

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FederatedScope: A Flexible Federated Learning Platform for Heterogeneity

PySyft is an open-source multi-language library enabling secure and private machine learning by wrapping and extending popular deep learning frameworks such as PyTorch in a transparent, lightweight, and user-friendly manner. Its aim is to both help popularize privacy-preserving techniques in machine learning by making them as accessible as possible via Python bindings and common tools familiar to researchers and data scientists, as well as to be extensible such that new Federated Learning (FL), Multi-Party Computation, or Differential Privacy methods can be flexibly and simply implemented and integrated. This chapter will introduce the methods available within the PySyft library and describe their implementations. We will then provide a proof-of-concept demonstration of a FL workflow using an example of how to train a convolutional neural network. Next, we review the use of PySyft in academic literature to date and discuss future use-cases and development plans. Most importantly, we introduce Duet: our tool for easier FL for scientists and data owners.

Antonio Lopardo

Papers

PySyft: A Library for Easy Federated Learning