Aerospace engineering

Operating system

Enhanced Data Rates for GSM Evolution

Intersection (aeronautics)

Biology

Computer science

Cloud computing

Engineering

Resource (disambiguation)

Genetics

Drone

Scalability

Distributed computing

Edge device

Artificial intelligence

Edge computing

Data science

Database

Computer network

IEEE Internet of Things Journal

Federated learning (FL) is a distributed machine learning strategy that generates a global model by learning from multiple decentralized edge clients. FL enables on-device training, keeping the client's local data private, and further, updating the global model based on the local model updates. While FL methods offer several advantages, including scalability and data privacy, they assume there are available computational resources at each edge-device/client. However, the Internet-of-Things (IoT)-enabled devices, e.g., robots, drone swarms, and low-cost computing devices (e.g., Raspberry Pi), may have limited processing ability, low bandwidth and power, or limited storage capacity. In this survey article, we propose to answer this question: how to train distributed machine learning models for resource-constrained IoT devices? To this end, we first explore the existing studies on FL, relative assumptions for distributed implementation using IoT devices, and explore their drawbacks. We then discuss the implement...

A Survey on Federated Learning for Resource-Constrained IoT Devices