A Survey on Federated Learning for Resource-Constrained IoT Devices

Federated learning (FL) is a distributed machine learning strategy that generates a global model by learning from multiple decentralized edge clients. We highlight an overview of FL and provide a comprehensive survey of the problem statements and emerging challenges, particularly during applying FL within heterogeneous IoT environments.

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• 1 FL enables on-device training, keeping the client’s local data private, and further, updating the global model based on the local model updates.
• 2 While FL methods offer several advantages, including scalability and data privacy, they assume there are available computational resources at each edge-device/client.
• 3 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.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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