Applications of Federated Learning in Smart Cities: Recent Advances, Taxonomy, and Open Challenges

Abstract

Federated learning plays an important role in the process of smart cities. With the development of big data and artificial intelligence, there is a problem of data privacy protection in this process. Federated learning is capable of solving this problem. This paper starts with the current developments of federated learning and its applications in various fields. We conduct a comprehensive investigation. This paper summarize the latest research on the application of federated learning in various fields of smart cities. In-depth understanding of the current development of federated learning from the Internet of Things, transportation, communications, finance, medical and other fields. Before that, we introduce the background, definition and key technologies of federated learning. Further more, we review the key technologies and the latest results. Finally, we discuss the future applications and research directions of federated learning in smart cities.

References in corpus (33)

• Communication-Efficient Learning of Deep Networks from Decentralized Data
• Federated Learning: Strategies for Improving Communication Efficiency
• Federated Learning with Non-IID Data
• Federated Optimization: Distributed Machine Learning for On-Device Intelligence
• In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning
• Towards Federated Learning at Scale: System Design
• Machine Learning at the Wireless Edge: Distributed Stochastic Gradient Descent Over-the-Air
• Optimal Distributed Online Prediction using Mini-Batches
• Differentially Private Federated Learning: A Client Level Perspective
• Distributed Federated Learning for Ultra-Reliable Low-Latency Vehicular Communications
• Federated Optimization in Heterogeneous Networks
• Fair Resource Allocation in Federated Learning
• Federated Meta-Learning with Fast Convergence and Efficient Communication
• Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems
• Threats to Federated Learning: A Survey
• Split learning for health: Distributed deep learning without sharing raw patient data
• Federated Adversarial Domain Adaptation
• Privacy Preserving Distributed Machine Learning with Federated Learning
• Distributed Mean Estimation with Limited Communication
• Battery-constrained Federated Edge Learning in UAV-enabled IoT for B5G/6G Networks
• Federated Multi-Task Learning
• Experiments of Federated Learning for COVID-19 Chest X-ray Images
• Dopamine: Differentially Private Federated Learning on Medical Data
• RedSync : Reducing Synchronization Traffic for Distributed Deep Learning
• Asymmetrical Vertical Federated Learning
• Robust Aggregation for Adaptive Privacy Preserving Federated Learning in Healthcare
• Towards Federated Graph Learning for Collaborative Financial Crimes Detection
• Dynamic Fusion based Federated Learning for COVID-19 Detection
• Interpret Federated Learning with Shapley Values
• Privacy-Preserving Asynchronous Federated Learning Algorithms for Multi-Party Vertically Collaborative Learning
• A Vertical Federated Learning Method for Interpretable Scorecard and Its Application in Credit Scoring
• Combining Federated and Active Learning for Communication-efficient Distributed Failure Prediction in Aeronautics
• Federated Learning Meets Contract Theory: Energy-Efficient Framework for Electric Vehicle Networks