The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learning

Abstract

The Internet of Things (IoT) is on the verge of a major paradigm shift. In the IoT system of the future, IoFT, the cloud will be substituted by the crowd where model training is brought to the edge, allowing IoT devices to collaboratively extract knowledge and build smart analytics/models while keeping their personal data stored locally. This paradigm shift was set into motion by the tremendous increase in computational power on IoT devices and the recent advances in decentralized and privacy-preserving model training, coined as federated learning (FL). This article provides a vision for IoFT and a systematic overview of current efforts towards realizing this vision. Specifically, we first introduce the defining characteristics of IoFT and discuss FL data-driven approaches, opportunities, and challenges that allow decentralized inference within three dimensions: (i) a global model that maximizes utility across all IoT devices, (ii) a personalized model that borrows strengths across all devices yet retains its own model, (iii) a meta-learning model that quickly adapts to new devices or learning tasks. We end by describing the vision and challenges of IoFT in reshaping different industries through the lens of domain experts. Those industries include manufacturing, transportation, energy, healthcare, quality & reliability, business, and computing.

Accepted at IEEE

References in corpus (25)

• Equality of Opportunity in Supervised Learning
• ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models
• Federated Optimization: Distributed Machine Learning for On-Device Intelligence
• Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning
• Meta-SGD: Learning to Learn Quickly for Few-Shot Learning
• Consistency of the group Lasso and multiple kernel learning
• Federated Learning with Personalization Layers
• Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption
• Collecting Telemetry Data Privately
• Agnostic Federated Learning
• Learning Task Grouping and Overlap in Multi-task Learning
• PCC: Re-architecting Congestion Control for Consistent High Performance
• Threats to Federated Learning: A Survey
• Sparsity in multiple kernel learning
• Federated Learning for Emoji Prediction in a Mobile Keyboard
• A Convex Formulation for Learning Task Relationships in Multi-Task Learning
• Federated Evaluation of On-device Personalization
• Federated Learning Of Out-Of-Vocabulary Words
• Focus: Querying Large Video Datasets with Low Latency and Low Cost
• Parallel Distributed Logistic Regression for Vertical Federated Learning without Third-Party Coordinator
• FedSplit: An algorithmic framework for fast federated optimization
• Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies
• Federated Learning for Ranking Browser History Suggestions
• Fairness and Accuracy in Federated Learning
• Improving Fairness via Federated Learning

Cited by in corpus (9)

• Generalizing intrusion detection for heterogeneous networks: A stacked-unsupervised federated learning approach
• GIFAIR-FL: A Framework for Group and Individual Fairness in Federated Learning
• Fed-ensemble: Improving Generalization through Model Ensembling in Federated Learning
• Federated Data Analytics: A Study on Linear Models
• Federated Gaussian Process: Convergence, Automatic Personalization and Multi-fidelity Modeling
• Collaborative and Distributed Bayesian Optimization via Consensus: Showcasing the Power of Collaboration for Optimal Design
• Differentially Private Distributed Estimation and Learning
• Personalized Tucker Decomposition: Modeling Commonality and Peculiarity on Tensor Data
• Collaborative and Federated Black-box Optimization: A Bayesian Optimization Perspective