A Survey on Federated Learning and its Applications for Accelerating Industrial Internet of Things
arXiv:2104.10501
Abstract
Federated learning (FL) brings collaborative intelligence into industries without centralized training data to accelerate the process of Industry 4.0 on the edge computing level. FL solves the dilemma in which enterprises wish to make the use of data intelligence with security concerns. To accelerate industrial Internet of things with the further leverage of FL, existing achievements on FL are developed from three aspects: 1) define terminologies and elaborate a general framework of FL for accommodating various scenarios; 2) discuss the state-of-the-art of FL on fundamental researches including data partitioning, privacy preservation, model optimization, local model transportation, personalization, motivation mechanism, platform & tools, and benchmark; 3) discuss the impacts of FL from the economic perspective. To attract more attention from industrial academia and practice, a FL-transformed manufacturing paradigm is presented, and future research directions of FL are given and possible immediate applications in Industry 4.0 domain are also proposed.
References in corpus (39)
- Communication-Efficient Learning of Deep Networks from Decentralized Data
- Federated Optimization: Distributed Machine Learning for On-Device Intelligence
- Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning
- A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection
- Federated Learning for Mobile Keyboard Prediction
- On the Convergence of FedAvg on Non-IID Data
- Privacy-preserving Traffic Flow Prediction: A Federated Learning Approach
- A Blockchain-based Decentralized Federated Learning Framework with Committee Consensus
- Secure Federated Transfer Learning
- Communication-Efficient Federated Deep Learning with Asynchronous Model Update and Temporally Weighted Aggregation
- Differentially Private Federated Learning: A Client Level Perspective
- Distributed Federated Learning for Ultra-Reliable Low-Latency Vehicular Communications
- Asynchronous Federated Optimization
- Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption
- Improving Federated Learning Personalization via Model Agnostic Meta Learning
- Personalized Federated Learning: A Meta-Learning Approach
- Adaptive Personalized Federated Learning
- Three Approaches for Personalization with Applications to Federated Learning
- Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems
- Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation System
- BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning
- Decentralized Federated Learning: A Segmented Gossip Approach
- Flower: A Friendly Federated Learning Research Framework
- Multi-Participant Multi-Class Vertical Federated Learning
- Privacy-preserving Federated Brain Tumour Segmentation
- Federated and Differentially Private Learning for Electronic Health Records
- Federated Learning for Ranking Browser History Suggestions
- A Federated Learning Approach for Mobile Packet Classification
- Quantifying the Performance of Federated Transfer Learning
- Dynamic Fusion based Federated Learning for COVID-19 Detection
- FedCoin: A Peer-to-Peer Payment System for Federated Learning
- Patient Clustering Improves Efficiency of Federated Machine Learning to predict mortality and hospital stay time using distributed Electronic Medical Records
- Adaptive Federated Learning and Digital Twin for Industrial Internet of Things
- FedLoc: Federated Learning Framework for Data-Driven Cooperative Localization and Location Data Processing
- Privacy-Preserving News Recommendation Model Learning
- Federated Learning for Localization: A Privacy-Preserving Crowdsourcing Method
- Robust Federated Training via Collaborative Machine Teaching using Trusted Instances
- Failure Prediction in Production Line Based on Federated Learning: An Empirical Study
- Federated Imitation Learning: A Privacy Considered Imitation Learning Framework for Cloud Robotic Systems with Heterogeneous Sensor Data